http://hdl.handle.net/10900/42133 2026-09-21T09:42:18Z http://hdl.handle.net/10900/183516 Untersuchung von zwei verwaisten RiPP-Biosynthese-Genclustern aus Chitinophaga dinghuensis und Ohtaekwangia koreensis Schröder, Maria Paula Ribosomally synthesized and posttranslationally modified peptides (RiPPs) represent a structurally diverse group of microbial natural products. Due to their potential antibiotic, antiviral, antifungal, or analgesic effects, there is great interest in the identification of new RiPPs. Particularly promising in this regard are so-called orphaned biosynthetic gene clusters (BGCs), whose natural product is still unknown. The aim of this work was the experimental investigation of two orphaned RiPP BGCs from Chitinophaga dinghuensis (chd cluster) and Ohtaekwangia koreensis (ohk cluster). Both clusters were first analyzed bioinformatically and annotated using BLAST. Supplementary structure predictions provided only limited additional insights. This suggests that the maturases under investigation are functionally specialized or enzymes that have not yet been sufficiently characterized. In the chd cluster, the focus was on the functional analysis of the predicted maturases. In addition to the previously described peptide arginase ChdR, the putative lanthionine dehydratase ChdB and the two lanthionine cyclases ChdC1 and ChdC2 were investigated. ChdR catalyzes the conversion of peptidically bound arginines to ornithine, while ChdB, in combination with ChdC1 and ChdC2, presumably forms two ring structures. For the ohk cluster, the central focus was on the biochemical characterization of the peptide arginase OhkR from O. koreensis. Through heterologous (co-)expression, the arginase activity of OhkR was clearly confirmed in vivo and in vitro. The localization of the modifications and the determination of the reaction directionality revealed unusual C-to N-terminal processing. Kinetic analyses showed high substrate affinity with simultaneously low turnover rate. Furthermore, OhkR exhibited substrate tolerance toward non-native precursor peptides from a related BGC. Structural predictions and mutation analyses provided clues to sequence motifs that could be relevant for substrate recognition. Co-expressions were also performed with the other maturases of the ohk cluster, but no activity could be detected in these cases. Overall, this work provides new experimental insights into the enzymatic complexity of RiPP BGCs and contributes to the characterization of different maturases. The results establish a starting point for further analysis of both clusters and for the potential identification of the corresponding natural products.; Die Dissertation ist gesperrt bis zum 24. Juli 2028 !; Ribosomal synthetisierte und posttranslational modifizierte Peptide (RiPPs) stellen eine strukturell diverse Gruppe mikrobieller Naturstoffe dar. Aufgrund ihrer potenziellen antibiotischen, antiviralen, antimykotischen oder analgetischen Wirkungen besteht großes Interesse an der Identifizierung neuer RiPPs. Besonders vielversprechend sind dabei sogenannte verwaiste Biosynthese Gencluster (BGC), deren Naturstoff noch unbekannt ist. Ziel dieser Arbeit war die experimentelle Untersuchung zweier verwaister RiPP BGC aus Chitinophaga dinghuensis (chd‑Cluster) und Ohtaekwangia koreensis (ohk‑Cluster). Beide Cluster wurden zunächst bioinformatisch analysiert und mittels BLAST annotiert. Ergänzende Strukturvorhersagen lieferten trotz hoher Modellqualität nur begrenzte zusätzliche Erkenntnisse. Dies deutet darauf hin, dass es sich bei den untersuchten Maturasen um funktionell spezialisierte oder bislang unzureichend charakterisierte Enzyme handelt. Im chd Cluster lag der Fokus auf der funktionellen Analyse der vorhergesagten Maturasen. Neben der bereits beschriebenen Peptid Arginase ChdR wurden insbesondere die putative Lanthionin Dehydratase ChdB sowie die beiden Lanthionin Zyklasen ChdC1 und ChdC2 untersucht. ChdR katalysiert die Umwandlung peptidisch gebundener Arginine zu Ornithin, während ChdB in Kombination mit ChdC1 und ChdC2 vermutlich zwei Ringstrukturen bildet. Für das ohk Cluster lag der zentrale Schwerpunkt auf der biochemischen Charakterisierung der Peptid Arginase OhkR aus O. koreensis. Durch heterologe (Ko-)Expression konnte die Arginase‑Aktivität von OhkR eindeutig in vivo und in vitro bestätigt werden. Die Lokalisierung der Modifikationen sowie die Bestimmung der Reaktionsdirektionalität zeigten eine ungewöhnliche C‑ zu N‑terminale Prozessierung. Kinetische Analysen zeigten eine hohe Substrataffinität bei gleichzeitig niedriger Umsatzzahl. Darüber hinaus zeigte OhkR eine Substrattoleranz gegenüber nicht‑nativen Präkursor Peptiden aus einem verwandten BGC. Strukturvorhersagen und Mutationsanalysen lieferten Hinweise auf Sequenzmotive, die für die Substraterkennung relevant sein könnten. Mit den anderen Maturasen des ohk Clusters wurden ebenfalls Koexpressionen durchgeführt, hier konnten jedoch keine Aktivitäten nachgewiesen werden. Insgesamt liefert diese Arbeit neue experimentelle Einblicke in die enzymatische Vielfalt der RiPP‑Biosynthese und trägt zur funktionellen Charakterisierung ungewöhnlicher Maturasen bei. Die Ergebnisse schaffen eine Grundlage für weiterführende Untersuchungen der zugehörigen Biosynthesewege und für die zukünftige Aufklärung der zugehörigen Naturstoffe. 2028-07-24T00:00:00Z http://hdl.handle.net/10900/183400 Archaeomalacology of the Iron Age Site of Muweilah: Taphonomy and Sustenance Assessment Müller García, Inés de la Fortuna Archaeomalacology, the study of mollusk remains from archaeological contexts, provides key insights into past human-environment interactions, including resource use, technology, and cultural practices. The present study examines over 40,000 mollusk remains from the Iron Age II site of Muweilah (northern United Arab Emirates), offering a rare perspective on marine resource use in an inland desert settlement. The assemblage was analyzed with respect to taxonomy, morphometry, and taphonomy to reconstruct patterns of collection, processing, and use, as well as environmental constraints. Intra-site variation was assessed using quantitative and diversity measures across excavation contexts and compared with other Southeast Arabian sites. Results show selective exploitation of a limited range of food species, with processing and cooking practices strongly influencing preservation alongside shell morphology. Non-dietary use was limited: most bivalves were likely collected post-mortem, while larger gastropods were occasionally used for ornamentation or as containers. Smaller species may have arrived incidentally. Spatial analyses reveal clear intra-site differentiation corresponding to architectural divisions. Notably, the columned hall (Building II) is marked by high oyster frequencies and low species diversity, whereas other areas show greater diversity dominated by Marcia cordata, indicating functionally distinct spaces. Regional comparisons highlight strong similarity with nearby coastal sites. In contrast, assemblages from Oman and the eastern Gulf reflect greater variability, reflecting environmental and cultural influences. Taken together, the findings demonstrate how inland communities integrated coastal resources to mitigate subsistence risk while participating in wider networks of mobility and exchange. At the same time, intra-site variation points to structured spatial organization and potentially socially differentiated activity areas. The study underscores the value of archaeomalacological evidence for understanding adaptation, subsistence, and regional connectivity in Southeast Arabia. 2026-09-16T00:00:00Z http://hdl.handle.net/10900/183399 Transformations, Equivalences and Comparisons of Learning Problems Iacovissi, Laura While artificial intelligence (AI) has achieved striking practical success across diverse applications, the theoretical foundations of many modern methods remain incomplete. In particular, a rigorous account of how the individual components of an AI system influence the outcome of its (machine) learning process is yet to be established. These components are numerous and heterogeneous, contributing to the high complexity of AI models; consequently, there is no reason to expect a single approach to fully resolve this gap. Thus, the present thesis abstracts away from the particular data points, optimization algorithms, and other technical aspects of AI systems. Paying a price in specificity, we gain a significant advantage: a framework for theoretically characterizing the interplay between the core components of a machine learning problem — loss function, model class, and data-generating probability distribution. In particular, we pay specific attention to the latter, as it is an often overlooked component in many modern works. We prove two main types of results, namely Bayes risk equalities and inequalities, obtaining equivalences and comparisons of learning problems, respectively. As a preliminary analysis, we provide an exhaustive taxonomy of stochastic transformations of a problem’s data distribution by leveraging properties of Markov kernels, i.e., the objects used to model the transformations. Studying equalities, we then learn how different Markov kernel types lead to distinct consequences. For example, applying label noise to the data distribution yields a learning problem equivalent to one that retains the original data-generating probability but with a modified loss function. By contrast, attribute noise will jointly affect both loss and model class in the equivalent problem. These findings suggest that classical loss correction techniques for noisy data are only effective in the case of label noise, not for general attribute noise. We then compute what a generalized loss correction approach would entail for attribute noise. However, focusing solely on equivalences only partially explains how tweaking one component affects the learning outcome. Studying the inequalities, we explore when one learning problem can be considered superior to another. This challenge can be addressed in multiple ways, as problems can be compared by varying the probability distribution, model class, or loss. Previous work explored comparing conditional probabilities for every loss and model class, arriving at interesting characterization results nowadays grouped under the Blackwell-Sherman-Stein theorem. This work has found application in economics as well as learning theory, and nicely relates to classical results in information theory. Our contribution frames the existing results into the larger perspective of comparing learning problems instead of conditional probabilities. We then focus on comparing couples of model classes and losses for every probability, introducing a theory complementary to Blackwell’s work, revolving around establishing when a decision maker is better than another. We study this question by means of Bayes risk orderings, which are proved to be equivalent to the inclusion ordering on certain sets, called superprediction sets. These sets are defined starting from fixing a loss and model class, and therefore provide geometrical understanding to the question of comparing decision makers. We additionally prove sufficient conditions for the ordering to hold on certain types of loss and model classes, which are decided by the type of noisy data considered. Hence, these results additionally show the central role of knowing what are the data at hand so to be able to understand the related learning problem. This work advances theoretical understanding of machine learning systems as highly entangled entities. Its first set of results underscores the critical importance of choosing your data carefully, as they demonstrate that changing the distribution is equivalent to changing the loss and model class. This can cause strong mismatches between what one expects the machine to learn and what the machine actually learns. In particular, the equalities proved offer guidance for designing corrections that aim to achieve accurate learning in the presence of noisy data. The second set of contributions introduces a novel theory for comparison of models and losses, and in particular for comparing them before and after a transformation is applied to the learning problem. This is relevant in the field of model complexity, as it introduces a decision-theoretic inspired framework to assess whether a certain decision maker is more reliable (in terms of risk) than another under a certain type of noise. 2026-09-16T00:00:00Z http://hdl.handle.net/10900/183385 Nanoscale Investigation on Linear and Nonlinear Optical Properties in Layered and Nanostructured Materials Zhao, Yang This dissertation presents a systematic nanoscale investigation of linear and nonlinear optical properties in layered and nanostructured materials, with particular emphasis on how local structural variations determine optical responses under far-field and polarization-resolved excitation conditions. Twodimensional transition metal dichalcogenides (2D-TMDCs) are widely studied due to their strong excitonic effects, symmetry-dependent nonlinear optical responses, and promising applications in optoelectronic and valleytronic devices. In addition, plasmonic nanostructures provide strong light confinement and local electromagnetic field enhancement through localized surface plasmon resonances, making them important platforms for nanoscale photonics, sensing, and nonlinear optical enhancement. Therefore, these two material systems are chosen as representative platforms to investigate geometry-dependent optical responses. By combining confocal scanning optical microscopy, ultrafast nonlinear spectroscopy, Raman spectroscopy and back focal plane imaging, this work establishes direct correlations between morphology, symmetry, dipole orientation, and optical emission behavior.; Die Dissertation ist gesperrt bis zum 30. Juni 2028 ! 2028-06-30T00:00:00Z