Intrinsic and extrinsic mechanisms involved in treatment resistance and progression of bladder and prostate cancers

Prostate cancer (PCa) and urothelial cancer (UC) are the most commonly diagnosed urological cancers. Advanced PCa and UC are significant causes of cancer-related deaths. Effective treatment of advanced PCa and UC remains an unmet medical need due to emerging resistance. Therefore, a deeper understanding of resistance mechanisms in advanced cancers is of utmost importance. Resistance could be intrinsic, emerging from the cancer cell itself, or extrinsic, conferred by surrounding non-transformed cells in the tumor microenvironment (TME). This doctoral thesis is based on three publications entitled: 1) “Androgen Receptor Splice Variants Contribute to the Upregulation of DNA Repair in Prostate Cancer”; 2) “CDCP1 Expression Is Frequently Increased in Aggressive Urothelial Carcinoma and Promotes Urothelial Tumor Progression”; 3) “Adipocyte Precursor-Derived NRG1 Promotes Resistance to FGFR Inhibition in Urothelial Carcinoma”. In the first publication, androgen receptor splice variants (AR-Vs) were shown to be increased in advanced PCa compared to primary PCa. AR-Vs in PCa clinical samples were positively associated with increased DNA repair activity. LNCaP cells overexpressing AR-V7 were used as an in-vitro model and confirmed the findings in clinical data. In the second publication, CDCP1 expression was correlated with advanced UC stages and reduced overall survival in two UC patient cohorts. Murine bladder organoids overexpressing CDCP1, and a human BCa cell line, SCaBER, knocked-out of CDCP1 revealed the role of CDCP1 in promoting growth and migration. In the third publication, neuregulin 1 (NRG1) secreted from adipocyte precursor cell lines was shown to confer paracrine resistance to erdafitinib in FGFR-dependent bladder and lung cancer cell lines. Conditioned media from adipocyte precursors was found to activate the human epidermal growth factor 3 (HER3) pathway. Pertuzumab, an FDA-approved antibody that inhibits HER2/HER3 dimerization, was shown to reverse the resistance conferred by NRG1 against erdafitinib in-vitro and in-vivo. Conditioned media from UC-derived cancer-associated fibroblasts (CAFs) corroborated the resistance mechanism against erdafitinib. These studied mechanisms of resistance could pave the way to novel therapeutic strategies aiming at maximizing treatment efficacy and patient survival

Linguistically Aware and Augmentation-Driven Methods for Enhancing Natural Language Understanding

Current large language models excel in solving numerous complicated tasks, but are primarily optimized for the English language and popular application domains, delivering sub-optimal outputs for low-resource languages and specialized industry use cases. Also, they rely heavily on large amounts of training data and computing resources. In this work, we aim to tackle those issues by implementing smaller, less resource-intensive models which are trained in an informed way, leveraging linguistic knowledge about semantic and syntactic features. To this end, we investigate methods for linguistically informed pre-training, incorporating the model with additional semantic and syntactic knowledge prior to fine-tuning on the downstream task. We specifically consider token-level prediction tasks with high semantic and syntactic relevance, such as Part-of-Speech-Tagging and Synset Prediction based on semantic webs. Our experimental results show that smaller models perform on par with larger ones when being pre-trained on those tasks, suggesting that this method can contribute to making language modeling more efficient. Another direction of research is the creation of prototypical training corpora, exploiting both linguistic knowledge and the generative power of large pre-trained language models. We hypothesize that using those prototypical data sets in language model training will help reduce the total amount of data needed, while keeping a similar performance on the downstream task. This conjecture is being confirmed by experimental results, underlining the goal of this thesis. To further test our hypothesis, we evaluate the informed pre-training and data generation approaches in low-resource scenarios, namely on the tasks of Natural Language Inference and Contradiction Detection in German and Arabic. We find that language model performance in those domains can be significantly improved using the aforementioned methods. Machine Translation is also being introduced as an effective method to obtain training corpora in under-researched languages. Finally, we evaluate the efficiency of those approaches on the basis of three real-world use cases from the financial domain. We specifically look at Causality Detection, Critical Error Detection, as well as Contradiction Detection in financial reports. In all three cases, our methods provide a significant performance boost, combined with insights into the nature of language for this specific domain. Overall, this thesis significantly contributes to the language modeling research field, exploring options to improve current paradigms for specialized scenarios and with a resource-aware objective

Human Aspects in Secure Messaging

The widespread adoption of digital communication demands robust security and privacy protections, particularly through secure messaging systems that can protect personal and sensitive information. Despite advancements in end-to-end security, encryption, and anonymity, significant gaps remain in usability and user trust, limiting widespread adoption. This cumulative dissertation examines the human aspects of secure messaging systems through four peer-reviewed studies, addressing fundamental challenges in usability, trust establishment, and practical implementations. Diverse methodological approaches drive the research, including systematic protocol analysis with a focus on human aspects, large-scale empirical studies, and qualitative investigations, alongside the proposal and evaluation of improved technical implementations. First, a comprehensive systematization of knowledge establishes a unified framework for evaluating secure messaging protocols and “in-the-wild” tools, investigating critical gaps in current approaches. Second, an empirical study with 1047 participants examines fingerprint representation approaches for trust establishment. Third, qualitative research explores potential misconceptions in user mental models and trust for end-to-end security in general. Finally, a novel hardware-based approach utilizing NFC-enabled wearables demonstrates practical solutions for simplifying cryptographic key management while maintaining security. Key findings indicate that (1) trust establishment remains the cornerstone of secure messaging, as it requires user interaction and underpins the entire security guarantees; failure in this area compromises the system entirely. (2) traditional hex-based fingerprint representations significantly underperform in both attack detection and perceived usability compared to the proposed sentence-based representation, but also numeric representation, as commonly used outside cryptographic contexts, also proving more effective; (3) users mistrust messaging platforms and security features in general and substantially overestimate attackers while underestimating cryptographic capabilities; and (4) less invasive security mechanisms as with using wearables show promise for broader adoption. The findings align with current developments in secure messaging applications, where similar verification approaches are used. This work advances the field of usable security by bridging theoretical understanding with practical implementation, contributing to the development of more effective and accessible secure communication systems. The findings provide guidance for designing next-generation secure messaging solutions that balance robust security with user needs and capabilities

Amtliche Bekanntmachungen, 55. Jahrgang, Nr. 42

Korrektur einer Ausgabe der Amtlichen Bekanntmachungen: Wahlbekanntmachung für die Nachwahl eines Mitglieds in der Gruppe der Hochschullehrerinnen und Hochschullehrer zum Senat im Wahlkreis Katholisch-Theologische Fakultät der Rheinischen Friedrich-Wilhelms-Universität Bonn vom 17. Juli 202

Charakterisierung der Hämostaseaktivierung bei Thrombosen im Splanchnikusgebiet

Die Ätiologie der SVT ist heterogen, wobei MPN einen wichtigen Risikofaktor darstellen. Frühere Studien deuten darauf hin, dass Veränderungen im antikoagulatorischen PC-System am Pathomechanismus der SVT beteiligt sind. Da die Rolle des PC-Systems im Kontext der SVT jedoch bislang nicht ausreichend untersucht wurde, war es das Ziel dieser Arbeit, die Aktivierung der Hämostase bei SVT-Patienten, insbesondere hinsichtlich der Unterschiede zwischen Patienten mit und ohne begleitende MPN, zu charakterisieren. Konkret wurde die Hypothese geprüft, ob sich SVT-Patienten mit MPN bezüglich der Aktivierung des PC-Systems von Patienten ohne MPN unterscheiden. Dazu wurden die Plasmaspiegel der Schlüsselenzyme des PC-Systems, Thrombin und aktiviertes Protein C (APC), sowie weitere Marker der Gerinnungs- und Fibrinolyseaktivierung in einer Kohorte von 73 SVT-Patienten prospektiv untersucht, darunter 36 Patienten mit und 37 Patienten ohne nachgewiesener MPN. Zusätzlich wurden 30 gesunde Kontrollpersonen analysiert, um Referenzwerte zu erhalten. Da sich im Verlauf der Analysen zeigte, dass eine bestehende Antikoagulation die Messwerte beeinflusste, erfolgte eine Subgruppenanalyse, um den Einfluss der Antikoagulationsintensität (therapeutische, reduzierte oder keine Antikoagulation) näher zu untersuchen. SVT-Patienten mit begleitender MPN wiesen im Vergleich zu Patienten ohne MPN signifikant erhöhte Plasmaspiegel von APC (Median: 1,23 vs. 0,58 pmol/l) und Thrombin (Median: 0,49 vs. Um die diagnostische Aussagekraft der untersuchten Marker zu beurteilen, erfolgte zusätzlich eine ROC-Analyse. Dabei zeigte APC im Vergleich zu indirekten Thrombinmarkern wie TAT und F1+2 diskret bessere Werte bei der Differenzierung zwischen SVT-Patienten mit und ohne MPN. Ein klarer diagnostischer Vorteil ließ sich daraus entsprechend nicht ableiten. Die initiale Hypothese, dass sich SVT-Patienten mit begleitender MPN hinsichtlich der Aktivierung des PC-Systems von Patienten ohne MPN unterscheiden, konnte anhand der erhöhten APC- und Thrombinwerte bestätigt werden. Gleichzeitig weisen die Ergebnisse darauf hin, dass die klinisch häufig verwendeten D-Dimere möglicherweise nicht ausreichend sind, um die bestehende Gerinnungsaktivierung bei SVT-Patienten mit MPN zuverlässig zu erfassen. Die erhöhten Werte von APC und Thrombin trotz Antikoagulation könnten auf eine anhaltende Hyperkoagulabilität bei diesen Patienten hindeuten. Zukünftige Studien sollten daher untersuchen, ob APC und Thrombin ergänzend zur D-Dimer-Messung eine klinisch relevante Rolle in der Diagnostik und Therapieplanung bei Patienten mit SVT einnehmen könnten

Amtliche Bekanntmachungen, 55. Jahrgang, Nr. 54

Beschluss des Fakultätsrats der Agrar-, Ernährungs- und Ingenieurwissenschaftlichen Fakultät der Rheinischen Friedrich-Wilhelms-Universität Bonn vom 2. Juli 2025 zum Außerkraftsetzen der Prüfungsorganisationsordnung 2020 sowie von Prüfungsordnungen der Agrar-, Ernährungs- und Ingenieurwissenschaftlichen Fakultä

Compact Description for High Resolution Spatial Weather Extremes

Detecting local climate change signals, particularly within the context of extreme weather events, is challenging due to the significant internal variability of the climate system. This thesis, part of the BMBF-funded ClimXtreme Phase I project, aims to improve the signal-to-noise ratio of climate change signals during extreme weather events through advanced data compression methods. Our approach is twofold, emphasizing data-adaptive techniques and spectral decomposition. These methods are evaluated for their effectiveness in describing heat waves, droughts, and precipitation extremes. We first analyze Principal Component Analysis (PCA). The focus on extremes is achieved using the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019). Applying this method to daily temperature maxima and meteorological droughts of varying durations, we identify an effective technique for analyzing and compactly describing large-scale multivariate weather extremes. Additionally, we introduce the cross-TPDM to identify patterns of concurrent extremes across two variables. We propose an extreme pattern index (EPI) that provides a pattern-based spatial aggregation of extremes and demonstrate that a heat wave definition based on EPI effectively detects major heat waves across Europe. For addressing simultaneous extremes in two variables, we extend this approach by introducing the threshold-based EPI (TEPI). Using the European heat waves of 2003 and 2010 as examples, we show that TEPI describes the large-scale compound character of heat waves and droughts. Next, we examine wavelet transformation, specifically utilizing the complex dual-tree wavelet, which has proven efficient for precipitation fields (see Brune et al., 2021). Focusing on extreme precipitation, we find that the wavelet transform accurately represents these extremes without requiring specific adaptations to method or data. Comparing two European reanalyses (COSMO-REA6, CERRA) for their representation of hourly precipitation, we discover that CERRA cannot accurately resolve small-scale convective events. A scale-aware detection study for three regions in northern, southern and western Germany reveals consistent trends of increasing intensity in small-scale events during summer and in large-scale events during winter.Die Identifizierung lokaler Signale des Klimawandels, insbesondere im Zusammenhang mit extremen Wetterereignissen, ist aufgrund der hohen internen Variabilität des Klimasystems eine Herausforderung. Diese Dissertation ist Teil des vom BMBF geförderten Projektes ClimXtreme Phase I, mit dem Ziel das Signal-Rausch-Verhältnis von Klimasignalen bei Extremwetterereignissen durch den Einsatz fortgeschrittener Datenkompressionsverfahren zu verbessern. Unser Ansatz ist zweigeteilt und konzentriert sich auf datenadaptive Techniken und spektrale Zerlegung. Diese Methoden werden auf ihre Wirksamkeit bei der Beschreibung von Hitzewellen, Dürren und Niederschlagsextremen untersucht. Zunächst analysieren wir die Hauptkomponentenanalyse (PCA). Der Fokus auf Extreme wird durch die von Cooley und Thibaud (2019) vorgeschlagene Tail Pairwise Dependence Matrix (TPDM) erreicht. Durch die Anwendung dieser Methode auf tägliche Temperaturmaxima und meteorologische Dürren unterschiedlicher Dauer identifizieren wir eine effektive Technik zur Analyse und kompakten Beschreibung großräumiger multivariater Wetterextreme. Zusätzlich führen wir die Cross-TPDM ein, um Muster von gleichzeitigen Extremen in zwei Variablen zu identifizieren. Wir schlagen einen Extreme Pattern Index (EPI) vor, eine musterbasierte räumliche Aggregation von Extremen. Um gemeinsame Extreme in zwei Variablen zu erfassen, erweitern wir diesen Ansatz durch die Einführung eines Schwellwert-basierten EPI (TEPI). Am Beispiel der europäischen Hitzewellen von 2003 und 2010 zeigen wir, dass EPI und TEPI eine geeignete Beschreibung des großräumig vernetzten Charakters von Hitzewellen und Dürren geben. Als nächstes untersuchen wir die Wavelet-Transformation, insbesondere das komplexe Dual-Tree-Wavelet, das sich bereits als effizient für Niederschlagsfelder erwiesen hat (siehe Brune et al., 2021). Bei der Untersuchung extremer Niederschläge stellen wir fest, dass die Wavelet-Transformation diese Extreme gut abbildet, ohne dass spezifische Anpassungen der Methode oder der Daten erforderlich sind. Wir vergleichen zwei europäische Reanalysen (COSMO-REA6, CERRA) hinsichtlich ihrer Darstellung von stündlichem Niederschlag und zeigen, dass die Darstellung von kleinskaligen konvektiven Ereignisse in CERRA unszureichend ist. Eine skalen basierte Detektionsstudie für drei Regionen in Nord-, Süd- und Westdeutschland zeigt konsistente Trends zunehmender Intensität für kleinskalige Ereignisse im Sommer und für großskalige Ereignisse im Winter

Modelling Molecular Gas and Its Tracers Across Cosmic Time

Our understanding of the distant Universe and the processes governing galaxy formation and evolution largely stems from observing the light from stars and its interaction with the material surrounding them. However, an essential piece of this picture lies in the role of the interstellar medium (ISM) in shaping star formation within galaxies. Unveiling this aspect requires tracing the fuel for star formation – molecular gas. This thesis explores molecular gas in galaxies across cosmic time using cosmological simulations of galaxy formation. Simulating the molecular gas content of galaxies requires modelling various physical and chemical processes happening on a wide range of scales. On large scales, galaxy growth is affected by gas accretion from the cosmic web. On the other hand, molecular gas chemistry is regulated by conditions on small scales, which are beyond the resolving capabilities of large scale simulations needed to investigate the evolution of the cosmic molecular gas content. To tackle this multi-scale problem, we have developed a sub-grid model called HYACINTH – HYdrogen And Carbon chemistry in the INTerstellar medium in Hydro simulations – that can be embedded into large-scale cosmological simulations to track the abundances of molecular hydrogen (H2), and its carbon-based observational proxies, namely, carbon monoxide (CO), atomic carbon (C), and singly-ionised carbon (C+), on the fly. We have implemented HYACINTH into the RAMSES code to perform the MARIGOLD simulations. Our simulated cosmic H2 density is in excellent agreement with current observational constraints. Additionally, we find that low-mass (MH2 8 solar masses) galaxies contain nearly half of the cosmic H2 in the early Universe. However, the sensitivity of current instruments renders these galaxies “invisible”, indicating a potential underestimation of the cosmic H2 density in existing surveys. In recent years, the [CII] fine-structure line of C+ has emerged as a molecular gas tracer in the early Universe. As one of the brightest emission lines, it offers a unique window into the molecular ISM of distant galaxies, where conventional tracers like CO become observationally expensive. We tested the reliability of this line as a molecular gas tracer at different cosmic epochs. Our analysis reveals that the [CII]-molecular gas correlation is relatively weak in the first billion years of the Universe but grows in both strength and tightness over time. We further examined the time evolution of the [CII] luminosity function and the cosmic [CII] luminosity density, and found that faint (L[CII] 7 solar luminosities) galaxies contribute nearly half of the cosmic [CII] density in the early Universe. Overall, this thesis highlights the pivotal role of cosmological simulations in interpreting observations and providing crucial insights into the molecular gas reservoir of galaxies, that serves as the fuel for star formation across cosmic time

Amtliche Bekanntmachungen, 55. Jahrgang, Nr. 38

Dritte Ordnung zur Änderung der Prüfungsordnung für den konsekutiven Masterstudiengang „Biochemistry“ der Mathematisch-Naturwissenschaftlichen Fakultät der Rheinischen Friedrich-Wilhelms-Universität Bonn vom 3. Juli 202

Locality and fluctuation effects in ionic liquids

Amino acid (AA) based imidazolium ionic liquids (ILs) are promising materials in biocatalysis, electrochemistry, drug delivery, and green solvents due to their low volatility, high thermal stability, and good solubility. However, the microstructure and interactions of ILs are complex, involving charge transfer, polarization, hydrogen bonds (HB), and π-π interactions. Accurately characterizing their physical and chemical properties is crucial for optimizing their performance. While classical molecular dynamics (MD) methods have limitations in describing polarization and charge transfer, ab initio molecular dynamics (AIMD) based on first principles is a powerful tool for studying these properties. This thesis systematically investigates the application of AIMD to study AA-ILs, focusing on polarization behavior, vibrational spectra, and intermolecular interactions. AA-ILs exhibit a more complex charge distribution than traditional imidazolium ILs due to AA side chains. Using AIMD with different charge distribution schemes (Wannier, Blöchl, Löwdin, Mulliken, and Voronoi), we analyze electronic polarization effects. The Wannier method most accurately describes charge transfer, while Mulliken and Voronoi methods perform differently across ion combinations. The study shows that the π-electron cloud of the imidazolium ring plays a key role in polarization, while anions contribute less due to their lower polarizability. The Wannier and Voronoi methods showed good agreement in predicting relative intensities and overall profiles. The Voronoi method reduced the instability during the time evolution and improved the spectral accuracy. We analyzed the C-H stretching vibration of the imidazole ring, the CO vibration and the HB vibration of the AA side chain in ILs, providing insights into their molecular recognition and environmental response. In addition, the data of the theoretical spectra and the experimental data also showed good agreement. The physicochemical properties of ILs are influenced by their intermolecular interactions. AIMD calculations of radial distribution functions (RDFs) and combined distribution functions (CDFs) reveal short- and long-range interaction patterns. Hydrogen bonding between cations and anions is crucial for system stability, while π-π stacking of the imidazole ring affects structural order and fluidity of ILs. Different AA side chains alter the local structure: ILs with carboxyl groups form stable HB networks, while those with phenyl side chains show strong intermolecular stacking. These structural features influence viscosity, diffusivity, and solvation ability, and play a key role in protein stabilization and catalysis. This thesis provides a comprehensive study of the polarization effects, vibrational spectra, and intermolecular interactions in AA-ILs using AIMD, offering insights into their microscopic mechanisms. The findings contribute to the theoretical foundation of ILs and their application in biochemistry, electrochemistry, and materials science