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The work done during the PhD studies has been focused on measurements of distribution functions of rotating galaxies using integral field spectroscopy observations.
Throughout the main body of research presented here we have been using CALIFA (Calar Alto Legacy Integral Field Area) survey stellar velocity fields to obtain robust measurements of circular velocities for rotating galaxies of all morphological types. A crucial part of the work was enabled by well-defined CALIFA sample selection criteria: it enabled reconstructing sample-independent distributions of galaxy properties.
In Chapter 2, we measure the distribution in absolute magnitude - circular velocity space for a well-defined sample of 199 rotating CALIFA galaxies using their stellar kinematics. Our aim in this analysis is to avoid subjective selection criteria and to take volume and large-scale structure factors into account. Using stellar velocity fields instead of gas emission line kinematics allows including rapidly rotating early type galaxies. Our initial sample contains 277 galaxies with available stellar velocity fields and growth curve r-band photometry. After rejecting 51 velocity fields that could not be modelled due to the low number of bins, foreground contamination or significant interaction we perform Markov Chain Monte Carlo (MCMC) modelling of the velocity fields, obtaining the rotation curve and kinematic parameters and their realistic uncertainties. We perform an extinction correction and calculate the circular velocity v_circ accounting for pressure support a given galaxy has. The resulting galaxy distribution on the M_r - v_circ plane is then modelled as a mixture of two distinct populations, allowing robust and reproducible rejection of outliers, a significant fraction of which are slow rotators. The selection effects are understood well enough that the incompleteness of the sample can be corrected and the 199 galaxies can be weighted by volume and large-scale structure factors enabling us to fit a volume-corrected Tully-Fisher relation (TFR). More importantly, we also provide the volume-corrected distribution of galaxies in the M_r - v_circ plane, which can be compared with cosmological simulations. The joint distribution of the luminosity and circular velocity space densities, representative over the range of -20 > M_r > -22 mag, can place more stringent constraints on the galaxy formation and evolution scenarios than linear TFR fit parameters or the luminosity function alone.
In Chapter 3, we measure one of the marginal distributions of the M_r - v_circ distribution: the circular velocity function of rotating galaxies. The velocity function is a fundamental observable statistic of the galaxy population, being of a similar importance as the luminosity function, but much more difficult to measure. We present the first directly measured circular velocity function that is representative between 60 < v_circ < 320 km s^-1 for galaxies of all morphological types at a given rotation velocity. For the low mass galaxy population 60 < v_circ < 170 km s^-1, we use the HIPASS velocity function. For the massive galaxy population 170 < v_circ < 320 km s^-1, we use stellar circular velocities from CALIFA. The CALIFA velocity function includes homogeneous velocity measurements of both late and early-type rotation-supported galaxies. It has the crucial advantage of not missing gas-poor massive ellipticals that HI surveys are blind to. We show that both velocity functions can be combined in a seamless manner, as their ranges of validity overlap. The resulting observed velocity function is compared to velocity functions derived from cosmological simulations of the z = 0 galaxy population. We find that dark matter-only simulations show a strong mismatch with the observed VF. Hydrodynamic Illustris simulations fare better, but still do not fully reproduce observations.
In Chapter 4, we present some other work done during the PhD studies, namely, a method that improves the precision of specific angular measurements by combining simultaneous Markov Chain Monte Carlo modelling of ionised gas 2D velocity fields and HI linewidths. To test the method we use a sample of 25 galaxies from the Sydney-AAO Multi-object Integral field (SAMI) survey that had matching ALFALFA HI linewidths. Such a method allows constraining the rotation curve both in the inner regions of a galaxy and in its outskirts, leading to increased precision of specific angular momentum measurements. It could be used to further constrain the observed relation between galaxy mass, specific angular momentum and morphology (Obreschkow & Glazebrook 2014).
Mathematical and computational methods are presented in the appendices.
Causes for slow weathering and erosion in the steep, warm, monsoon-subjected Highlands of Sri Lanka
(2018)
In the Highlands of Sri Lanka, erosion and chemical weathering rates are among the lowest for global mountain denudation. In this tropical humid setting, highly weathered deep saprolite profiles have developed from high-grade metamorphic charnockite during spheroidal weathering of the bedrock. The spheroidal weathering produces rounded corestones and spalled rindlets at the rock-saprolite interface. I used detailed textural, mineralogical, chemical, and electron-microscopic (SEM, FIB, TEM) analyses to identify the factors limiting the rate of weathering front advance in the profile, the sequence of weathering reactions, and the underlying mechanisms. The first mineral attacked by weathering was found to be pyroxene initiated by in situ Fe oxidation, followed by in situ biotite oxidation. Bulk dissolution of the primary minerals is best described with a dissolution – re-precipitation process, as no chemical gradients towards the mineral surface and sharp structural boundaries are observed at the nm scale. Only the local oxidation in pyroxene and biotite is better described with an ion by ion process. The first secondary phases are oxides and amorphous precipitates from which secondary minerals (mainly smectite and kaolinite) form. Only for biotite direct solid state transformation to kaolinite is likely. The initial oxidation of pyroxene and biotite takes place in locally restricted areas and is relatively fast: log J = -11 molmin/(m2 s). However, calculated corestone-scale mineral oxidation rates are comparable to corestone-scale mineral dissolution rates: log R = -13 molpx/(m2 s) and log R = -15 molbt/(m2 s). The oxidation reaction results in a volume increase. Volumetric calculations suggest that this observed oxidation leads to the generation of porosity due to the formation of micro-fractures in the minerals and the bedrock allowing for fluid transport and subsequent dissolution of plagioclase. At the scale of the corestone, this fracture reaction is responsible for the larger fractures that lead to spheroidal weathering and to the formation of rindlets. Since these fractures have their origin from the initial oxidational induced volume increase, oxidation is the rate limiting parameter for weathering to take place. The ensuing plagioclase weathering leads to formation of high secondary porosity in the corestone over a distance of only a few cm and eventually to the final disaggregation of bedrock to saprolite. As oxidation is the first weathering reaction, the supply of O2 is a rate-limiting factor for chemical weathering. Hence, the supply of O2 and its consumption at depth connects processes at the weathering front with erosion at the surface in a feedback mechanism. The strength of the feedback depends on the relative weight of advective versus diffusive transport of O2 through the weathering profile. The feedback will be stronger with dominating diffusive transport. The low weathering rate ultimately depends on the transport of O2 through the whole regolith, and on lithological factors such as low bedrock porosity and the amount of Fe-bearing primary minerals. In this regard the low-porosity charnockite with its low content of Fe(II) bearing minerals impedes fast weathering reactions. Fresh weatherable surfaces are a pre-requisite for chemical weathering. However, in the case of the charnockite found in the Sri Lankan Highlands, the only process that generates these surfaces is the fracturing induced by oxidation. Tectonic quiescence in this region and low pre-anthropogenic erosion rate (attributed to a dense vegetation cover) minimize the rejuvenation of the thick and cohesive regolith column, and lowers weathering through the feedback with erosion.
New bio-based polymers
(2018)
Redox-responsive polymers, such as poly(disulfide)s, are a versatile class of polymers with potential applications including gene- and drug-carrier systems. Their degradability under reductive conditions allows for a controlled response to the different redox states that are present throughout the body. Poly(disulfide)s are typically synthesized by step growth polymerizations. Step growth polymerizations, however, may suffer from low conversions and therefore low molar masses, limiting potential applications. The purpose of this thesis was therefore to find and investigate new synthetic routes towards the synthesis of amino acid-based poly(disulfide)s.
The different routes in this thesis include entropy-driven ring opening polymerizations of novel macrocyclic monomers, derived from cystine derivatives. These monomers were obtained with overall yields of up to 77% and were analyzed by mass spectrometry as well as by 1D and 2D NMR spectroscopy. The kinetics of the entropy-driven ring-opening metathesis polymerization (ED-ROMP) were thoroughly investigated in dependence of temperature, monomer concentration, and catalyst concentration. The polymerization was optimized to yield poly(disulfide)s with weight average molar masses of up to 80 kDa and conversions of ~80%, at the thermodynamic equilibrium. Additionally, an alternative metal free polymerization, namely the entropy-driven ring-opening disulfide metathesis polymerization (ED-RODiMP) was established for the polymerization of the macrocyclic monomers. The effect of different solvents, concentrations and catalyst loadings on the polymerization process and its kinetics were studied. Polymers with very high weight average molar masses of up to 177 kDa were obtained. Moreover, various post-polymerization reactions were successfully performed.
This work provides the first example of the homopolymerization of endo-cyclic disulfides by ED-ROMP and the first substantial study into the kinetics of the ED-RODiMP process.
Exploring elections features from a geographical perspective is the focus of this study. Its primary objective is to develop a scientific approach based on geoinformation technology (GIT) that promotes deeper understanding how geographical settings affect the spatial and temporal variations of voting behaviour and election outcomes. For this purpose, the five parliamentary elections (1991-2005) following the political turnaround in 1990 in the South East European reform country Albania have been selected as a case study. Elections, like other social phenomena that do not develop uniformly over a territory, inherit a spatial dimension. Despite of fact that elections have been researched by various scientific disciplines ranging from political science to geography, studies that incorporate their spatial dimension are still limited in number and approaches. Consequently, the methodologies needed to generate an integrated knowledge on many facets that constitute election features are lacking. This study addresses characteristics and interactions of the essential elements involved in an election process. Thus, the baseline of the approach presented here is the exploration of relations between three entities: electorate (political and sociodemographic features), election process (electoral system and code) and place (environment where voters reside). To express this interaction the concept of electoral pattern is introduced. Electoral patterns are defined by the study as the final view of election results, chiefly in tabular and/or map form, generated by the complex interaction of social, economic, juridical, and spatial features of the electorate, which has occurred at a specific time and in a particular geographical location. GIT methods of geoanalysis and geovisualization are used to investigate the characteristics of electoral patterns in their spatial and temporal distribution. Aggregate-level data modelled in map form were used to analyse and visualize the spatial distribution of election patterns components and relations. The spatial dimension of the study is addressed in the following three main relations: One, the relation between place and electorate and its expression through the social, demographic and economic features of the electorate resulting in the profile of the electorate’s context; second, the electorate-election interaction which forms the baseline to explore the perspective of local contextual effects in voting behaviour and election results; third, the relation between geographical location and election outcomes reflecting the implication of determining constituency boundaries on election results. To address the above relations, three types of variables: geo, independent and dependent, have been elaborated and two models have been created. The Data Model, developed in a GIS environment, facilitates structuring of election data in order to perform spatial analysis. The peculiarity of electoral patterns – a multidimensional array that contains information on three variables, stored in data layers of dissimilar spatial units of reference and scales of value measurement – prohibit spatial analysis based on the original source data. To perform a joint spatial analysis it is therefore mandatory to restructure the spatial units of reference while preserving their semantic content. In this operation, all relevant electoral as well as socio-demographic data referenced to different administrative spatial entities are re-referenced to uniform grid cells as virtual spatial units of reference. Depending on the scale of data acquisition and map presentation, a cell width of 0.5 km has been determined. The resulting fine grid forms the basis of subsequent data analyses and correlations. Conversion of the original vector data layers into target raster layers allows for unification of spatial units, at the same time retaining the existing level of detail of the data (variables, uniform distribution over space). This in turn facilitates the integration of the variables studied and the performance of GIS-based spatial analysis. In addition, conversion to raster format makes it possible to assign new values to the original data, which are based on a common scale eliminating existing differences in scale of measurement. Raster format operations of the type described are well-established data analysis techniques in GIT, yet they have rarely been employed to process and analyse electoral data. The Geovisualization Model, developed in a cartographic environment, complements the Data Model. As an analog graphic model it facilitates efficient communication and exploration of geographical information through cartographic visualization. Based on this model, 52 choropleth maps have been generated. They represent the outcome of the GIS-based electoral data analysis. The analog map form allows for in-depth visual analysis and interpretation of the distribution and correlation of the electoral data studied. For researchers, decision makers and a wider public the maps provide easy-to-access information on and promote easy-to-understand insight into the spatial dimension, regional variation and resulting structures of the electoral patterns defined.
The cytoskeletal motor protein kinesin-1 (conventional kinesin) is the fast carrier for intracellular cargo transport along microtubules. So far most studies aimed at investigating the transport properties of individual motor molecules. However, the transport in cells usually involves the collective work of more than one motor. In the present work, we have studied the movement of beads as artificial loads/organelles pulled by several kinesin-1 motors in vitro. For a wide range of motor coverage of the beads and different bead (cargo) sizes the transport parameters walking distance or run length, velocity and force generation are measured. The results indicate that the transport parameters are influenced by the number of motors carrying the bead. While the transport velocity slightly decreases, an increase in the run length was measured and higher forces are determined, when more motors are involved. The effective number of motors pulling a bead is estimated by measuring the change in the hydrodynamic diameter of kinesin-coated beads using dynamic light scattering. The geometrical constraints imposed by the transport system have been taken into account. Thus, results for beads of different size and motor-surface coverage could be compared. In addition, run length-distributions obtained for the smallest bead size were matched to theoretically calculated distributions. The latter yielded an average number of pulling motors, which is in agreement with the effective motor numbers determined experimentally.
In this thesis, we give two constructions for Riemannian metrics on Seiberg-Witten moduli spaces. Both these constructions are naturally induced from the L2-metric on the configuration space. The construction of the so called quotient L2-metric is very similar to the one construction of an L2-metric on Yang-Mills moduli spaces as given by Groisser and Parker. To construct a Riemannian metric on the total space of the Seiberg-Witten bundle in a similar way, we define the reduced gauge group as a subgroup of the gauge group. We show, that the quotient of the premoduli space by the reduced gauge group is isomorphic as a U(1)-bundle to the quotient of the premoduli space by the based gauge group. The total space of this new representation of the Seiberg-Witten bundle carries a natural quotient L2-metric, and the bundle projection is a Riemannian submersion with respect to these metrics. We compute explicit formulae for the sectional curvature of the moduli space in terms of Green operators of the elliptic complex associated with a monopole. Further, we construct a Riemannian metric on the cobordism between moduli spaces for different perturbations. The second construction of a Riemannian metric on the moduli space uses a canonical global gauge fixing, which represents the total space of the Seiberg-Witten bundle as a finite dimensional submanifold of the configuration space. We consider the Seiberg-Witten moduli space on a simply connected Käuhler surface. We show that the moduli space (when nonempty) is a complex projective space, if the perturbation does not admit reducible monpoles, and that the moduli space consists of a single point otherwise. The Seiberg-Witten bundle can then be identified with the Hopf fibration. On the complex projective plane with a special Spin-C structure, our Riemannian metrics on the moduli space are Fubini-Study metrics. Correspondingly, the metrics on the total space of the Seiberg-Witten bundle are Berger metrics. We show that the diameter of the moduli space shrinks to 0 when the perturbation approaches the wall of reducible perturbations. Finally we show, that the quotient L2-metric on the Seiberg-Witten moduli space on a Kähler surface is a Kähler metric.
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.
Hyperspectral remote sensing of the spatial and temporal heterogeneity of low Arctic vegetation
(2019)
Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have implications for ecosystem functioning as well as the global carbon and energy balance. Optical remote sensing is an effective tool for monitoring ecosystem functioning in this remote biome. However, limited field-based spectral characterization of the spatial and temporal heterogeneity limits the accuracy of quantitative optical remote sensing at landscape scales. To address this research gap and support current and future satellite missions, three central research questions were posed:
• Does canopy-level spectral variability differ between dominant low Arctic vegetation communities and does this variability change between major phenological phases?
• How does canopy-level vegetation colour images recorded with high and low spectral resolution devices relate to phenological changes in leaf-level photosynthetic pigment concentrations?
• How does spatial aggregation of high spectral resolution data from the ground to satellite scale influence low Arctic tundra vegetation signatures and thereby what is the potential of upcoming hyperspectral spaceborne systems for low Arctic vegetation characterization?
To answer these questions a unique and detailed database was assembled. Field-based canopy-level spectral reflectance measurements, nadir digital photographs, and photosynthetic pigment concentrations of dominant low Arctic vegetation communities were acquired at three major phenological phases representing early, peak and late season. Data were collected in 2015 and 2016 in the Toolik Lake Research Natural Area located in north central Alaska on the North Slope of the Brooks Range. In addition to field data an aerial AISA hyperspectral image was acquired in the late season of 2016. Simulations of broadband Sentinel-2 and hyperspectral Environmental and Mapping Analysis Program (EnMAP) satellite reflectance spectra from ground-based reflectance spectra as well as simulations of EnMAP imagery from aerial hyperspectral imagery were also obtained.
Results showed that canopy-level spectral variability within and between vegetation communities differed by phenological phase. The late season was identified as the most discriminative for identifying many dominant vegetation communities using both ground-based and simulated hyperspectral reflectance spectra. This was due to an overall reduction in spectral variability and comparable or greater differences in spectral reflectance between vegetation communities in the visible near infrared spectrum.
Red, green, and blue (RGB) indices extracted from nadir digital photographs and pigment-driven vegetation indices extracted from ground-based spectral measurements showed strong significant relationships. RGB indices also showed moderate relationships with chlorophyll and carotenoid pigment concentrations. The observed relationships with the broadband RGB channels of the digital camera indicate that vegetation colour strongly influences the response of pigment-driven spectral indices and digital cameras can track the seasonal development and degradation of photosynthetic pigments.
Spatial aggregation of hyperspectral data from the ground to airborne, to simulated satel-lite scale was influenced by non-photosynthetic components as demonstrated by the distinct shift of the red edge to shorter wavelengths. Correspondence between spectral reflectance at the three scales was highest in the red spectrum and lowest in the near infra-red. By artificially mixing litter spectra at different proportions to ground-based spectra, correspondence with aerial and satellite spectra increased. Greater proportions of litter were required to achieve correspondence at the satellite scale.
Overall this thesis found that integrating multiple temporal, spectral, and spatial data is necessary to monitor the complexity and heterogeneity of Arctic tundra ecosystems. The identification of spectrally similar vegetation communities can be optimized using non-peak season hyperspectral data leading to more detailed identification of vegetation communities. The results also highlight the power of vegetation colour to link ground-based and satellite data. Finally, a detailed characterization non-photosynthetic ecosystem components is crucial for accurate interpretation of vegetation signals at landscape scales.
Business process management is an acknowledged asset for running an organization in a productive and sustainable way. One of the most important aspects of business process management, occurring on a daily basis at all levels, is decision making. In recent years, a number of decision management frameworks have appeared in addition to existing business process management systems. More recently, Decision Model and Notation (DMN) was developed by the OMG consortium with the aim of complementing the widely used Business Process Model and Notation (BPMN). One of the reasons for the emergence of DMN is the increasing interest in the evolving paradigm known as the separation of concerns. This paradigm states that modeling decisions complementary to processes reduces process complexity by externalizing decision logic from process models and importing it into a dedicated decision model. Such an approach increases the agility of model design and execution. This provides organizations with the flexibility to adapt to the ever increasing rapid and dynamic changes in the business ecosystem. The research gap, identified by us, is that the separation of concerns, recommended by DMN, prescribes the externalization of the decision logic of process models in one or more separate decision models, but it does not specify this can be achieved.
The goal of this thesis is to overcome the presented gap by developing a framework for discovering decision models in a semi-automated way from information about existing process decision making. Thus, in this thesis we develop methodologies to extract decision models from: (1) control flow and data of process models that exist in enterprises; and (2) from event logs recorded by enterprise information systems, encapsulating day-to-day operations. Furthermore, we provide an extension of the methodologies to discover decision models from event logs enriched with fuzziness, a tool dealing with partial knowledge of the process execution information. All the proposed techniques are implemented and evaluated in case studies using real-life and synthetic process models and event logs. The evaluation of these case studies shows that the proposed methodologies provide valid and accurate output decision models that can serve as blueprints for executing decisions complementary to process models. Thus, these methodologies have applicability in the real world and they can be used, for example, for compliance checks, among other uses, which could improve the organization's decision making and hence it's overall performance.
One third of the world's population lives in areas where earthquakes causing at least slight damage are frequently expected. Thus, the development and testing of global seismicity models is essential to improving seismic hazard estimates and earthquake-preparedness protocols for effective disaster-risk mitigation. Currently, the availability and quality of geodetic data along plate-boundary regions provides the opportunity to construct global models of plate motion and strain rate, which can be translated into global maps of forecasted seismicity. Moreover, the broad coverage of existing earthquake catalogs facilitates in present-day the calibration and testing of global seismicity models. As a result, modern global seismicity models can integrate two independent factors necessary for physics-based, long-term earthquake forecasting, namely interseismic crustal strain accumulation and sudden lithospheric stress release.
In this dissertation, I present the construction of and testing results for two global ensemble seismicity models, aimed at providing mean rates of shallow (0-70 km) earthquake activity for seismic hazard assessment. These models depend on the Subduction Megathrust Earthquake Rate Forecast (SMERF2), a stationary seismicity approach for subduction zones, based on the conservation of moment principle and the use of regional "geodesy-to-seismicity" parameters, such as corner magnitudes, seismogenic thicknesses and subduction dip angles. Specifically, this interface-earthquake model combines geodetic strain rates with instrumentally-recorded seismicity to compute long-term rates of seismic and geodetic moment. Based on this, I derive analytical solutions for seismic coupling and earthquake activity, which provide this earthquake model with the initial abilities to properly forecast interface seismicity. Then, I integrate SMERF2 interface-seismicity estimates with earthquake computations in non-subduction zones provided by the Seismic Hazard Inferred From Tectonics based on the second iteration of the Global Strain Rate Map seismicity approach to construct the global Tectonic Earthquake Activity Model (TEAM). Thus, TEAM is designed to reduce number, and potentially spatial, earthquake inconsistencies of its predecessor tectonic earthquake model during the 2015-2017 period. Also, I combine this new geodetic-based earthquake approach with a global smoothed-seismicity model to create the World Hybrid Earthquake Estimates based on Likelihood scores (WHEEL) model. This updated hybrid model serves as an alternative earthquake-rate approach to the Global Earthquake Activity Rate model for forecasting long-term rates of shallow seismicity everywhere on Earth.
Global seismicity models provide scientific hypotheses about when and where earthquakes may occur, and how big they might be. Nonetheless, the veracity of these hypotheses can only be either confirmed or rejected after prospective forecast evaluation. Therefore, I finally test the consistency and relative performance of these global seismicity models with independent observations recorded during the 2014-2019 pseudo-prospective evaluation period. As a result, hybrid earthquake models based on both geodesy and seismicity are the most informative seismicity models during the testing time frame, as they obtain higher information scores than their constituent model components. These results support the combination of interseismic strain measurements with earthquake-catalog data for improved seismicity modeling. However, further prospective evaluations are required to more accurately describe the capacities of these global ensemble seismicity models to forecast longer-term earthquake activity.
Climatic variations and human activity now and increasingly in the future cause land cover changes and introduce perturbations in the terrestrial carbon reservoirs in vegetation, soil and detritus. Optical remote sensing and in particular Imaging Spectroscopy has shown the potential to quantify land surface parameters over large areas, which is accomplished by taking advantage of the characteristic interactions of incident radiation and the physico-chemical properties of a material. The objective of this thesis is to quantify key soil parameters, including soil organic carbon, using field and Imaging Spectroscopy. Organic carbon, iron oxides and clay content are selected to be analyzed to provide indicators for ecosystem function in relation to land degradation, and additionally to facilitate a quantification of carbon inventories in semiarid soils. The semiarid Albany Thicket Biome in the Eastern Cape Province of South Africa is chosen as study site. It provides a regional example for a semiarid ecosystem that currently undergoes land changes due to unadapted management practices and furthermore has to face climate change induced land changes in the future. The thesis is divided in three methodical steps. Based on reflectance spectra measured in the field and chemically determined constituents of the upper topsoil, physically based models are developed to quantify soil organic carbon, iron oxides and clay content. Taking account of the benefits limitations of existing methods, the approach is based on the direct application of known diagnostic spectral features and their combination with multivariate statistical approaches. It benefits from the collinearity of several diagnostic features and a number of their properties to reduce signal disturbances by influences of other spectral features. In a following step, the acquired hyperspectral image data are prepared for an analysis of soil constituents. The data show a large spatial heterogeneity that is caused by the patchiness of the natural vegetation in the study area that is inherent to most semiarid landscapes. Spectral mixture analysis is performed and used to deconvolve non-homogenous pixels into their constituent components. For soil dominated pixels, the subpixel information is used to remove the spectral influence of vegetation and to approximate the pure spectral signature coming from the soil. This step is an integral part when working in natural non-agricultural areas where pure bare soil pixels are rare. It is identified as the largest benefit within the multi-stage methodology, providing the basis for a successful and unbiased prediction of soil constituents from hyperspectral imagery. With the proposed approach it is possible (1) to significantly increase the spatial extent of derived information of soil constituents to areas with about 40 % vegetation coverage and (2) to reduce the influence of materials such as vegetation on the quantification of soil constituents to a minimum. Subsequently, soil parameter quantities are predicted by the application of the feature-based soil prediction models to the maps of locally approximated soil signatures. Thematic maps showing the spatial distribution of the three considered soil parameters in October 2009 are produced for the Albany Thicket Biome of South Africa. The maps are evaluated for their potential to detect erosion affected areas as effects of land changes and to identify degradation hot spots in regard to support local restoration efforts. A regional validation, carried out using available ground truth sites, suggests remaining factors disturbing the correlation of spectral characteristics and chemical soil constituents. The approach is developed for semiarid areas in general and not adapted to specific conditions in the study area. All processing steps of the developed methodology are implemented in software modules, where crucial steps of the workflow are fully automated. The transferability of the methodology is shown for simulated data of the future EnMAP hyperspectral satellite. Soil parameters are successfully predicted from these data despite intense spectral mixing within the lower spatial resolution EnMAP pixels. This study shows an innovative approach to use Imaging Spectroscopy for mapping of key soil constituents, including soil organic carbon, for large areas in a non-agricultural ecosystem and under consideration of a partially vegetation coverage. It can contribute to a better assessment of soil constituents that describe ecosystem processes relevant to detect and monitor land changes. The maps further provide an assessment of the current carbon inventory in soils, valuable for carbon balances and carbon mitigation products.
Black shales are sedimentary rocks with a high content of organic carbon, which leads to a dark grayish to black color. Due to their potential to contain oil or gas, black shales are of great interest for the support of the worldwide energy supply. An integrated seismic investigation of the Lower Palaeozoic black shales was carried out at the Danish island Bornholm to locate the shallow-lying Alum Shale layer and its surrounding formations and to characterize its potential as a source rock. Therefore, two seismic experiments at a total of three crossing profiles were carried out in October 2010 and in June 2012 in the southern part of the island. Two different active measurements were conducted with either a weight drop source or a minivibrator. Additionally, the ambient noise field was recorded at the study location over a time interval of about one day, and also a laboratory analysis of borehole samples was carried out. The seismic profiles were positioned as close as possible to two scientific boreholes which were used for comparative purposes. The seismic field data was analyzed with traveltime tomography, surface wave inversion and seismic interferometry to obtain the P-wave and S-wave velocity models of the subsurface. The P-wave velocity models which were determined for all three profiles clearly locate the Alum Shale layer between the Komstad Limestone layer on top and the Læså Sandstone Formation at the base of the models. The black shale layer has P-wave velocities around 3 km/s which are lower compared to the adjacent formations. Due to a very good agreement of the sonic log and the vertical velocity profiles of the two seismic lines, which are directly crossing the borehole where the sonic log was conducted, the reliability of the traveltime tomography is proven. A correlation of the seismic velocities with the content of organic carbon is an important task for the characterization of the reservoir properties of a black shale formation. It is not possible without calibration but in combination with a full 2D tomographic image of the subsurface it gives the subsurface distribution of the organic material. The S-wave model obtained with surface wave inversion of the vibroseis data of one of the profiles images the Alum Shale layer also very well with S-wave velocities around 2 km/s. Although individual 1D velocity models for each of the source positions were determined, the subsurface S-wave velocity distribution is very uniform with a good match between the single models. A really new approach described here is the application of seismic interferometry to a really small study area and a quite short time interval. Also new is the selective procedure of only using time windows with the best crosscorrelation signals to achieve the final interferograms. Due to the small scale of the interferometry even P-wave signals can be observed in the final crosscorrelations. In the laboratory measurements the seismic body waves were recorded for different pressure and temperature stages. Therefore, samples of different depths of the Alum Shale were available from one of the scientific boreholes at the study location. The measured velocities have a high variance with changing pressure or temperature. Recordings with wave propagation both parallel and perpendicular to the bedding of the samples reveal a great amount of anisotropy for the P-wave velocity, whereas the S-wave velocity is almost independent of the wave direction. The calculated velocity ratio is also highly anisotropic with very low values for the perpendicular samples and very high values for the parallel ones. Interestingly, the laboratory velocities of the perpendicular samples are comparable to the velocities of the field experiments indicating that the field measurements are sensitive to wave propagation in vertical direction. The velocity ratio is also calculated with the P-wave and S-wave velocity models of the field experiments. Again, the Alum Shale can be clearly separated from the adjacent formations because it shows overall very low vP/vS ratios around 1.4. The very low velocity ratio indicates the content of gas in the black shale formation. With the combination of all the different methods described here, a comprehensive interpretation of the seismic response of the black shale layer can be made and the hydrocarbon source rock potential can be estimated.
The thesis assesses the contribution of technology option of Carbon Capture and Sequestration (CCS) to climate change mitigation. CCS means that CO2 is captured at large industrial facilities and sequestered in goelogical structures. The technology uses the endogenous growth model MIND. Herein the various climate change mitigation options of reducing economic growth, increasing energy efficiency, changing the energy mix and CCS are assessed simultaneously. An important question is whether CCS is a temporary or long-term solution. The results show that in the middle of the 21st century CCS has its peak contribution, which allows prolonged use of relatively cheap fossil energy carriers. However, this leads to delayed introduction of renewable energy carriers. The technology path ways are accombined with different costs of climate change mitigation. The use of CCS delays and reduces the costs of climate change mitigation. However, the delayed introduction of renewable energy carriers leads to reduced technological learning, which induces higher costs in the longer term. All in all the temporary use of CCS reduces the costs of climate change mitigation costs. The result is robust, which is tested with various uncertainty analysis.
Data integration aims to combine data of different sources and to provide users with a unified view on these data. This task is as challenging as valuable. In this thesis we propose algorithms for dependency discovery to provide necessary information for data integration. We focus on inclusion dependencies (INDs) in general and a special form named conditional inclusion dependencies (CINDs): (i) INDs enable the discovery of structure in a given schema. (ii) INDs and CINDs support the discovery of cross-references or links between schemas. An IND “A in B” simply states that all values of attribute A are included in the set of values of attribute B. We propose an algorithm that discovers all inclusion dependencies in a relational data source. The challenge of this task is the complexity of testing all attribute pairs and further of comparing all of each attribute pair's values. The complexity of existing approaches depends on the number of attribute pairs, while ours depends only on the number of attributes. Thus, our algorithm enables to profile entirely unknown data sources with large schemas by discovering all INDs. Further, we provide an approach to extract foreign keys from the identified INDs. We extend our IND discovery algorithm to also find three special types of INDs: (i) Composite INDs, such as “AB in CD”, (ii) approximate INDs that allow a certain amount of values of A to be not included in B, and (iii) prefix and suffix INDs that represent special cross-references between schemas. Conditional inclusion dependencies are inclusion dependencies with a limited scope defined by conditions over several attributes. Only the matching part of the instance must adhere the dependency. We generalize the definition of CINDs distinguishing covering and completeness conditions and define quality measures for conditions. We propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. The challenge for this task is twofold: (i) Which (and how many) attributes should be used for the conditions? (ii) Which attribute values should be chosen for the conditions? Previous approaches rely on pre-selected condition attributes or can only discover conditions applying to quality thresholds of 100%. Our approaches were motivated by two application domains: data integration in the life sciences and link discovery for linked open data. We show the efficiency and the benefits of our approaches for use cases in these domains.
Business process management is an established technique for business organizations to manage and support their processes. Those processes are typically represented by graphical models designed with modeling languages, such as the Business Process Model and Notation (BPMN).
Since process models do not only serve the purpose of documentation but are also a basis for implementation and automation of the processes, they have to satisfy certain correctness requirements. In this regard, the notion of soundness of workflow nets was developed, that can be applied to BPMN process models in order to verify their correctness. Because the original soundness criteria are very restrictive regarding the behavior of the model, different variants of the soundness notion have been developed for situations in which certain violations are not even harmful.
All of those notions do only consider the control-flow structure of a process model, however. This poses a problem, taking into account the fact that with the recent release and the ongoing development of the Decision Model and Notation (DMN) standard, an increasing number of process models are complemented by respective decision models. DMN is a dedicated modeling language for decision logic and separates the concerns of process and decision logic into two different models, process and decision models respectively.
Hence, this thesis is concerned with the development of decisionaware soundness notions, i.e., notions of soundness that build upon the original soundness ideas for process models, but additionally take into account complementary decision models. Similar to the various notions of workflow net soundness, this thesis investigates different notions of decision soundness that can be applied depending on the desired degree of restrictiveness. Since decision tables are a standardized means of DMN to represent decision logic, this thesis also puts special focus on decision tables, discussing how they can be translated into an unambiguous format and how their possible output values can be efficiently determined.
Moreover, a prototypical implementation is described that supports checking a basic version of decision soundness. The decision soundness notions were also empirically evaluated on models from participants of an online course on process and decision modeling as well as from a process management project of a large insurance company. The evaluation demonstrates that violations of decision soundness indeed occur and can be detected with our approach.
Surface displacement at volcanic edifices is related to subsurface processes associated with magma movements, fluid transfers within the volcano edifice and gravity-driven deformation processes. Understanding of associated ground displacements is of importance for assessment of volcanic hazards. For example, volcanic unrest is often preceded by surface uplift, caused by magma intrusion and followed by subsidence, after the withdrawal of magma. Continuous monitoring of the surface displacement at volcanoes therefore might allow the forecasting of upcoming eruptions to some extent. In geophysics, the measured surface displacements allow the parameters of possible deformation sources to be estimated through analytical or numerical modeling. This is one way to improve the understanding of subsurface processes acting at volcanoes. Although the monitoring of volcanoes has significantly improved in the last decades (in terms of technical advancements and number of monitored volcanoes), the forecasting of volcanic eruptions remains puzzling. In this work I contribute towards the understanding of the subsurface processes at volcanoes and thus to the improvement of volcano eruption forecasting. I have investigated the displacement field of Llaima volcano in Chile and of Tendürek volcano in East Turkey by using synthetic aperture radar interferometry (InSAR). Through modeling of the deformation sources with the extracted displacement data, it was possible to gain insights into potential subsurface processes occurring at these two volcanoes that had been barely studied before. The two volcanoes, although of very different origin, composition and geometry, both show a complexity of interacting deformation sources. At Llaima volcano, the InSAR technique was difficult to apply, due to the large decorrelation of the radar signal between the acquisition of images. I developed a model-based unwrapping scheme, which allows the production of reliable displacement maps at the volcano that I used for deformation source modeling. The modeling results show significant differences in pre- and post-eruptive magmatic deformation source parameters. Therefore, I conjecture that two magma chambers exist below Llaima volcano: a post-eruptive deep one and a shallow one possibly due to the pre-eruptive ascent of magma. Similar reservoir depths at Llaima have been confirmed by independent petrologic studies. These reservoirs are interpreted to be temporally coupled. At Tendürek volcano I have found long-term subsidence of the volcanic edifice, which can be described by a large, magmatic, sill-like source that is subject to cooling contraction. The displacement data in conjunction with high-resolution optical images, however, reveal arcuate fractures at the eastern and western flank of the volcano. These are most likely the surface expressions of concentric ring-faults around the volcanic edifice that show low magnitudes of slip over a long time. This might be an alternative mechanism for the development of large caldera structures, which are so far assumed to be generated during large catastrophic collapse events. To investigate the potential subsurface geometry and relation of the two proposed interacting sources at Tendürek, a sill-like magmatic source and ring-faults, I have performed a more sophisticated numerical modeling approach. The optimum source geometries show, that the size of the sill-like source was overestimated in the simple models and that it is difficult to determine the dip angle of the ring-faults with surface displacement data only. However, considering physical and geological criteria a combination of outward-dipping reverse faults in the west and inward-dipping normal faults in the east seem to be the most likely. Consequently, the underground structure at the Tendürek volcano consists of a small, sill-like, contracting, magmatic source below the western summit crater that causes a trapdoor-like faulting along the ring-faults around the volcanic edifice. Therefore, the magmatic source and the ring-faults are also interpreted to be temporally coupled. In addition, a method for data reduction has been improved. The modeling of subsurface deformation sources requires only a relatively small number of well distributed InSAR observations at the earth’s surface. Satellite radar images, however, consist of several millions of these observations. Therefore, the large amount of data needs to be reduced by several orders of magnitude for source modeling, to save computation time and increase model flexibility. I have introduced a model-based subsampling approach in particular for heterogeneously-distributed observations. It allows a fast calculation of the data error variance-covariance matrix, also supports the modeling of time dependent displacement data and is, therefore, an alternative to existing methods.
Core-shell upconversion nanoparticles - investigation of dopant intermixing and surface modification
(2022)
Frequency upconversion nanoparticles (UCNPs) are inorganic nanocrystals capable to up-convert incident photons of the near-infrared electromagnetic spectrum (NIR) into higher energy photons. These photons are re-emitted in the range of the visible (Vis) and even ultraviolet (UV) light. The frequency upconversion process (UC) is realized with nanocrystals doped with trivalent lanthanoid ions (Ln(III)). The Ln(III) ions provide the electronic (excited) states forming a ladder-like electronic structure for the Ln(III) electrons in the nanocrystals. The absorption of at least two low energy photons by the nanoparticle and the subsequent energy transfer to one Ln(III) ion leads to the promotion of one Ln(III) electron into higher excited electronic states. One high energy photon will be emitted during the radiative relaxation of the electron in the excited state back into the electronic ground state of the Ln(III) ion. The excited state electron is the result of the previous absorption of at least two low energy photons.
The UC process is very interesting in the biological/medical context. Biological samples (like organic tissue, blood, urine, and stool) absorb high-energy photons (UV and blue light) more strongly than low-energy photons (red and NIR light). Thanks to a naturally occurring optical window, NIR light can penetrate deeper than UV light into biological samples. Hence, UCNPs in bio-samples can be excited by NIR light. This possibility opens a pathway for in vitro as well as in vivo applications, like optical imaging by cell labeling or staining of specific organic tissue. Furthermore, early detection and diagnosis of diseases by predictive and diagnostic biomarkers can be realized with bio-recognition elements being labeled to the UCNPs. Additionally, "theranostic" becomes possible, in which the identification and the treatment of a disease are tackled simultaneously.
For this to succeed, certain parameters for the UCNPs must be met: high upconversion efficiency, high photoluminescence quantum yield, dispersibility, and dispersion stability in aqueous media, as well as availability of functional groups to introduce fast and easy bio-recognition elements. The UCNPs used in this work were prepared with a solvothermal decomposition synthesis yielding in particles with NaYF4 or NaGdF4 as host lattice. They have been doped with the Ln(III) ions Yb3+ and Er3+, which is only one possible upconversion pair. Their upconversion efficiency and photoluminescence quantum yield were improved by adding a passivating shell to reduce surface quenching.
However, the brightness of core-shell UCNPs stays behind the expectations compared to their bulk material (being at least μm-sized particles). The core-shell structures are not clearly separated from each other, which is a topic in literature. Instead, there is a transition layer between the core and the shell structure, which relates to the migration of the dopants within the host lattice during the synthesis. The ion migration has been examined by time-resolved laser spectroscopy and the interlanthanoid resonance energy transfer (LRET) in the two different host lattices from above. The results are
presented in two publications, which dealt with core-shell-shell structured nanoparticles. The core is doped with the LRET-acceptor (either Nd3+ or Pr3+). The intermediate shell serves as an insulation shell of pure host lattice material, whose shell thickness has been varied within one set of samples having the same composition, so that the spatial separation of LRET-acceptor and -donor changes. The outer shell with the same host lattice is doped with the LRET-donor (Eu3+). The effect of the increasing insulation shell thickness is significant, although the LRET cannot be suppressed completely.
Next to the Ln(III) migration within a host lattice, various phase transfer reactions were investigated in order to subsequently perform surface modifications for bioapplications. One result out of this research has been published using a promising ligand, that equips the UCNP with bio-modifiable groups and has good potential for bio-medical applications. This particular ligand mimics natural occurring mechanisms of mussel protein adhesion and of blood coagulation, which is why the UCNPs are encapsulated very effectively. At the same time, bio-functional groups are introduced. In a proof-of-concept, the encapsulated UCNP has been coupled successfully with a dye (which is representative for a biomarker) and the system’s photoluminescence properties have been investigated.
Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, including small average path length, large clustering coefficient, heavy-tail degree distribution, and hierarchical organization, viewed as requirements for efficient and robust system architectures. Existing studies estimate the significance of network properties using a generic randomization scheme - a Markov-chain switching algorithm - which generates unrealistic reactions in metabolic networks, as it does not account for the physical principles underlying metabolism. Therefore, it is unclear whether the properties identified with this generic approach are related to the functions of metabolic networks. Within this doctoral thesis, I have developed an algorithm for mass-balanced randomization of metabolic networks, which runs in polynomial time and samples networks almost uniformly at random. The properties of biological systems result from two fundamental origins: ubiquitous physical principles and a complex history of evolutionary pressure. The latter determines the cellular functions and abilities required for an organism’s survival. Consequently, the functionally important properties of biological systems result from evolutionary pressure. By employing randomization under physical constraints, the salient structural properties, i.e., the smallworld property, degree distributions, and biosynthetic capabilities of six metabolic networks from all kingdoms of life are shown to be independent of physical constraints, and thus likely to be related to evolution and functional organization of metabolism. This stands in stark contrast to the results obtained from the commonly applied switching algorithm. In addition, a novel network property is devised to quantify the importance of reactions by simulating the impact of their knockout. The relevance of the identified reactions is verified by the findings of existing experimental studies demonstrating the severity of the respective knockouts. The results suggest that the novel property may be used to determine the reactions important for viability of organisms. Next, the algorithm is employed to analyze the dependence between mass balance and thermodynamic properties of Escherichia coli metabolism. The thermodynamic landscape in the vicinity of the metabolic network reveals two regimes of randomized networks: those with thermodynamically favorable reactions, similar to the original network, and those with less favorable reactions. The results suggest that there is an intrinsic dependency between thermodynamic favorability and evolutionary optimization. The method is further extended to optimizing metabolic pathways by introducing novel chemically feasibly reactions. The results suggest that, in three organisms of biotechnological importance, introduction of the identified reactions may allow for optimizing their growth. The approach is general and allows identifying chemical reactions which modulate the performance with respect to any given objective function, such as the production of valuable compounds or the targeted suppression of pathway activity. These theoretical developments can find applications in metabolic engineering or disease treatment. The developed randomization method proposes a novel approach to measuring the significance of biological network properties, and establishes a connection between large-scale approaches and biological function. The results may provide important insights into the functional principles of metabolic networks, and open up new possibilities for their engineering.
Text is a ubiquitous entity in our world and daily life. We encounter it nearly everywhere in shops, on the street, or in our flats. Nowadays, more and more text is contained in digital images. These images are either taken using cameras, e.g., smartphone cameras, or taken using scanning devices such as document scanners. The sheer amount of available data, e.g., millions of images taken by Google Streetview, prohibits manual analysis and metadata extraction. Although much progress was made in the area of optical character recognition (OCR) for printed text in documents, broad areas of OCR are still not fully explored and hold many research challenges. With the mainstream usage of machine learning and especially deep learning, one of the most pressing problems is the availability and acquisition of annotated ground truth for the training of machine learning models because obtaining annotated training data using manual annotation mechanisms is time-consuming and costly. In this thesis, we address of how we can reduce the costs of acquiring ground truth annotations for the application of state-of-the-art machine learning methods to optical character recognition pipelines. To this end, we investigate how we can reduce the annotation cost by using only a fraction of the typically required ground truth annotations, e.g., for scene text recognition systems. We also investigate how we can use synthetic data to reduce the need of manual annotation work, e.g., in the area of document analysis for archival material. In the area of scene text recognition, we have developed a novel end-to-end scene text recognition system that can be trained using inexact supervision and shows competitive/state-of-the-art performance on standard benchmark datasets for scene text recognition. Our method consists of two independent neural networks, combined using spatial transformer networks. Both networks learn together to perform text localization and text recognition at the same time while only using annotations for the recognition task. We apply our model to end-to-end scene text recognition (meaning localization and recognition of words) and pure scene text recognition without any changes in the network architecture.
In the second part of this thesis, we introduce novel approaches for using and generating synthetic data to analyze handwriting in archival data. First, we propose a novel preprocessing method to determine whether a given document page contains any handwriting. We propose a novel data synthesis strategy to train a classification model and show that our data synthesis strategy is viable by evaluating the trained model on real images from an archive. Second, we introduce the new analysis task of handwriting classification. Handwriting classification entails classifying a given handwritten word image into classes such as date, word, or number. Such an analysis step allows us to select the best fitting recognition model for subsequent text recognition; it also allows us to reason about the semantic content of a given document page without the need for fine-grained text recognition and further analysis steps, such as Named Entity Recognition. We show that our proposed approaches work well when trained on synthetic data. Further, we propose a flexible metric learning approach to allow zero-shot classification of classes unseen during the network’s training. Last, we propose a novel data synthesis algorithm to train off-the-shelf pixel-wise semantic segmentation networks for documents. Our data synthesis pipeline is based on the famous Style-GAN architecture and can synthesize realistic document images with their corresponding segmentation annotation without the need for any annotated data!
The life cycle of higher plants is based on recurring phases of growth and development based on repetitive sequences of cell division, cell expansion and cell differentiation. This dissertation deals with two projects, each of them investigating two different topics that are related to cell expansion. The first project is examining an Arabidopsis thaliana mutant exhibiting overall cell enlargement and the second project is analysing two naturally occurring floral morphs of Amsinckia spectabilis (Boraginaceae) differing (amongst others) in style length and anther heights due to differences in longitudinal cell elongation. The EMS-mutant eop1 was shown to exhibit a petal size increase of 26% caused by cell enlargement. Further phenotypes were detected, such as cotyledon size increase (based on larger cells) as well as increased carpel, sepal, leaf and pollen sizes. Plant height was shown to be increased and more highly branched trichomes explained the hairy eop1 phenotype. Fine mapping revealed the causal SNP to be a C to T transition at the last nucleotide of intron 7 of the INCURVATA11 (ICU11) gene, a 2-oxoglutarate /Fe(II)-dependant dioxygenase, and thus causing missplicing of the mRNA. Two T-DNA insertion lines (icu11-2 & icu11-4) confirmed ICU11 as causal gene by exhibiting increased petal size. A comparison of three icu11 alleles, which possessed different mutation-related changes, either overexpressing ICU11 or modified mRNAs, was the base for investigating the molecular mechanism that underlies the observed phenotype. Different approaches revealed contradictory results regarding ICU11 protein functionality in the icu11 mutants. A complementation assay proved the three mutants to be exchangeable and ICU11 overexpression in the wild-type led to an icu11-like phenotype, arguing for all three icu11 mutants to be GOF mutants. Contradicting this conclusion, the icu11-4 line could be rescued by a genomic ICU11 transgene. A model, based on the assumption that an overexpression of ICU11 is inhibiting the function of the protein, and thus causing the same effect as a LOF protein was proposed. Further, icu11-3 (eop1) mutants were shown to have an increased resistance towards paclobutrazol, a gibberellin (GA) inhibitor and an upregulation of AtGA20ox2, a main GA biosynthesis gene. Additionally, ICU11 subcellular localization was discovered to be cytoplasmic, supporting the assumption, that ICU11 affects GA biosynthesis and overall GA level, possibly explaining the observed (GA-overdose) phenotype.
The second project aimed to identify the genetic base of the S-locus in Amsinckia spectabilis, as the Amsinckia genus represents untypical characteristics for a heterostylous species, such as no obvious self-incompatibility (SI) and the repeated transition towards homostylous and fully selfing variants. The work was based on three Amsinckia spectabilis forms: a heterostylous form, consisting of two floral morphs with reciprocal positioning of sexual organs (S-morph: high anthers and a short style and L-morph: low anthers and a long style), and two homostylous forms, one large-flowered and partially selfing and the other small-flowered and fully selfing. The maintenance of the two floral morphs is genetically based on the S-locus region, containing genes that encode for the morph-specific traits, which are marked by a tight linkage due to suppressed recombination. Natural populations are found to possess a 1:1 S:L morph ratio, that can be explained by predominant disassortative mating of the two morphs, causing the occurrence of the dominant S-allele only in the heterozygous state (heterozygous (Ss) for the S-morph and homozygous recessive (ss) for the L-morph). Investigation of morph-specific phenotypes detected 56% elongated L-morph styles and 58% higher positioned S-morph anthers. Approximately 50% of the observed size differences were explained by an increase in cell elongation. Moreover, additional phenotypes were found, such as 21% enlarged S-morph pollen and no obvious SI, confirmed by hand pollinated seed counts, in vivo pollen tube growth and the development of homozygous dominant SS individuals via selfing. The Amsinckia spec. S-locus was assumed to at least consist of the G- (style length), the A- (anther height) and the P- (pollen size) locus. Comparative Transcriptomics of the two morphs revealed 22 differentially expressed markers that were found to be located within two contigs of a SS individual PacBio genome assembly, allowing the localization of the S-locus to be delimited to a region of approximately 23 Mb. Contradictory to revealed S-loci within the plant kingdom, no strong argument for a present hemizygous region was found to be causal for the suppressed recombination of the S-locus, so that an inversion was assumed to be the causal mechanism.