Human vocal communication and music perception represent advanced cognitive skills, seemingly innate and universal. These faculties encompass a range of perceptual and cognitive abilities. Cross... Show moreHuman vocal communication and music perception represent advanced cognitive skills, seemingly innate and universal. These faculties encompass a range of perceptual and cognitive abilities. Cross-species research sheds light on the origins of musicality by investigating whether these traits are shared by nonhuman species. Songbirds, notably zebra finches, serve as valuable models due to their complex vocalizations and similarities to humans in auditory perception. My thesis explored zebra finches' sensitivity to spectral and temporal sound features. Chapter 2 examines the influence of song duration and spectral characteristics on song discrimination, while Chapter 3 tests song preferences. Chapter 4 investigates sequential and spectral feature recognition. Chapter 5 focuses on melody recognition. Zebra finches demonstrate perceptual flexibility, adapting focus based on stimulus characteristics. These findings underscore the importance of training conditions and stimulus nature in shaping auditory perception. Overall, my thesis enhances understanding of auditory cognition and cognitive flexibility among songbirds. Show less
This thesis introduces the concept of "physics-based inverse design", working on the notion that the physical driving forces governing functionality are inherently encoded in independently... Show moreThis thesis introduces the concept of "physics-based inverse design", working on the notion that the physical driving forces governing functionality are inherently encoded in independently parameterized energy functions, which can be resolved through the use of inverse design strategies.The thesis describes the development of EVO-MD, a Python-based implementation of the physics-based inverse design concept. EVO-MD is capable of automatically setting-up, performing, and analyzing molecular dynamics simulations, allowing for the evolutionary optimization of complex and dynamic features in peptides. Examples of such applications include the optimization of lipid composition and curvature sensors, and the development of peptides with antiviral properties. Show less
This thesis aims to investigate the effect of tripartite interaction between microbial inoculants, the plant, and herbivore insects on the rhizosphere microbiome and volatilome. We investigated the... Show moreThis thesis aims to investigate the effect of tripartite interaction between microbial inoculants, the plant, and herbivore insects on the rhizosphere microbiome and volatilome. We investigated the rhizosphere microbiome and volatilome of tomato plants exposed to insect herbivory and/or inoculated with beneficial microbes known to trigger ISR. First, we reviewed the abiotic and biotic factors that impact the success of ISR microbial inoculants (Chapter 2). Then, we tested microbial inoculants against different stresses and experimental conditions to compare interactions in variable contexts (Chapter 3). Next, we explored the impact of insect herbivory aboveground, on the volatile and microbial belowground compartment. In Chapter 4 we explored root volatiles under stress in two tomato species to evaluate the genotype impact on the stress-induced root volatilome. In Chapter 5, we studied the impact of endosymbiotic fungi arbuscular mycorrhizal fungi (AMF) on root volatiles in an in vitro bioassay and in a greenhouse setup with herbivory-stressed plants. In Chapter 6, we compared the effect of four phylogenetically diverse bacteria and fungi, inoculated as single-species and as a synthetic community, on the rhizosphere microbiome assembly and volatilome in herbivory-stressed plants. Overall, this thesis delves into overlooked interactions providing novel data on belowground plant-microbe interactions. Show less
In the current global context, there is a pressing need to address sustainable energy supplies to safeguard our Planet and its ecosystems. The choices made by human society have a significant... Show moreIn the current global context, there is a pressing need to address sustainable energy supplies to safeguard our Planet and its ecosystems. The choices made by human society have a significant impact on genetic evolution and climate. To ensure a better future for all, it is crucial to exercise foresight, foster collaboration across various sectors, and reach agreements based on fair and ethical principles. Science plays a pivotal role in advancing energy conversion, offering the potential for significant scientific breakthroughs that contribute to the protection and respect of our World. Specifically, the development of solar-to-fuel devices holds promise for achieving this transition to green energy. This Ph.D. dissertation centers on the development and functionalization of 2D membranes and materials, which constitute integral components of these conversion devices. The optimization of functionalized 2D materials necessitates a comprehensive computational design approach. This involves the adoption of a multiscale computational framework for the thorough design of these materials and the precise prediction and understanding of molecular processes, encompassing molecular self-assembly, ion transport, and catalytic surface reactions. Show less
We show that Kirchhoff ’s law of conservation holds for non-commutative graph flows if and only if the graph is planar. We generalize the theory of (Euclidean) lattices to infinite dimension and... Show moreWe show that Kirchhoff ’s law of conservation holds for non-commutative graph flows if and only if the graph is planar. We generalize the theory of (Euclidean) lattices to infinite dimension and consider the ring of algebraic integers as such a lattice. We compute some invariants using capacity theory and obtain a partial solution to the (algorithmic) closest vector problem. We generalize the results on (universally) graded rings by Lenstra and Silverberg. We study the special case of group rings, and show that under similar assumptions rings can be uniquely decomposed into a group ring in a maximal way. We give a functorial algorithm to compute roots of fractional ideals of orders in number rings. Show less
Polymyxins are clinically used antibiotics, discovered in mid-20th century. Once abandoned due to excessive nephrotoxicity, they are now used increasingly to address infections caused by multi-drug... Show morePolymyxins are clinically used antibiotics, discovered in mid-20th century. Once abandoned due to excessive nephrotoxicity, they are now used increasingly to address infections caused by multi-drug resistant Gram-negative bacteria.In this thesis, we describe the development and synthesis of analogues of polymyxin, aimed at reducing its associated nephrotoxicity. Analogues were made by semisynthesis, with modifications introduced mostly in the exocyclic portion of the molecule. Especially the introduction of a disulfide bond within the linked lipid helped in reducing the toxicity of the molecules, as evidenced by testing on proximal tubule epithelial cells. For most potent analogues, the antimicrobial activity was completely retained.In addition, this thesis describes studies on the mechanism of action of polymyxin, mostly based on the full stereoisomer of polymyxin B4. This analogue lacks antimicrobial activity, indicating its original stereochemistry to be of utmost importance for its use as an antibiotic.Hybrids based on polymyxin B derivatives are described, addressing non-conventional targets. A hybrid with vancomycin (typically active on Gram-positive bacteria only) shows activity on Gram-negative bacteria. A polymyxin-based hybrid coupled to a peptide with a beta-hairpin motif addresses Gram-negative bacteria, presumably by binding to outer membrane protein BamA. Show less
Understanding how galaxies form, interact, and evolve comes largely from comparing theory predictions with observational data. Numerical simulations of galaxies provide the most accurate approach... Show moreUnderstanding how galaxies form, interact, and evolve comes largely from comparing theory predictions with observational data. Numerical simulations of galaxies provide the most accurate approach to testing the theory, as they follow the non-linear evolution of gas and dark matter in great detail and incorporate numerous baryonic processes, among which are energy feedback from supernovae (SNe) and Active Galactic Nuclei (AGN). In this thesis, we show the results of the development of the new model COLIBRE for cosmological simulations of galaxy formation that include a cold interstellar medium. First, we present a new SN feedback recipe developed for COLIBRE, whereby SN energy is injected into the gas in thermal and kinetic forms, and the total energy and momentum of the system of gas and stars are exactly conserved. Second, we conduct a detailed comparison of different ways in which SN energy is distributed in the gas environment around young stellar populations. Third, by using our simulation setup originally developed to test COLIBRE’s SN feedback, we show that the radioactive isotope Fe60 that has been detected on Earth is likely of SN origin. Finally, we present the calibration of the SN and AGN feedback of the COLIBRE model using machine learning. Show less
The research presented in this thesis explores the chemotherapeutic potential of metal-based compounds as chemotherapy agents, with an initial focus on the synthesis and DNA interaction studies of... Show moreThe research presented in this thesis explores the chemotherapeutic potential of metal-based compounds as chemotherapy agents, with an initial focus on the synthesis and DNA interaction studies of platinum and palladium compounds utilizing the [Pt(bapbpy)]2+ scaffold. The study identifies intercalation as the primary mechanism of action for these complexes. Furthermore, it provides a detailed structure-activity relationship analysis, highlighting the critical role of the complex's protonation state in influencing its biological activity and efficacy. Subsequently, the study delves into photoactivated chemotherapy (PACT) using ruthenium (II) complexes, where light activation of ruthenium complexes enables targeted drug delivery to tumor cells, thereby reducing adverse effects. This research emphasizes the development of ruthenium-based compounds that can photorelease a DNA repair inhibitor, specifically targeting the RAD51 protein, essential for Homologous Recombination (HR). By disrupting the DNA repair mechanisms in cancer cells, this approach seeks to enhance the cytotoxicity of the therapy and address drug resistance. Show less
Quantum computing is an emerging technology, which holds the potential to simulate complex quantum systems beyond the reach of classical numerical methods.Despite recent formidable advancements in... Show moreQuantum computing is an emerging technology, which holds the potential to simulate complex quantum systems beyond the reach of classical numerical methods.Despite recent formidable advancements in quantum hardware, constructing a quantum computer capable of performing useful calculations remains challenging.In the absence of a reliable quantum computer, the study of potential applications relies on mathematical methods, ingenious approximations, and heuristics derived from the fields of application. This thesis focuses on developing new quantum algorithms, targeting some of the key challenges in the simulation of complex quantum systems.The techniques introduced in this thesis span from quantum state preparation to mitigation of hardware and algorithmic noise, from efficient expectation value measurement to noise-resilient applications in quantum chemistry. A common thread connecting all these algorithms is the introduction of a single auxiliary qubit – a fundamental unit of quantum information – which has an active and distinctive role in the task at hand. Show less
Learning from small data sets in machine learning is a crucial challenge, especially when dealing with data imbalances and anomaly detection. This thesis delves into the challenges and... Show moreLearning from small data sets in machine learning is a crucial challenge, especially when dealing with data imbalances and anomaly detection. This thesis delves into the challenges and methodologies of learning from small datasets in machine learning, with a particular focus on addressing data imbalances and anomaly detec- tion. It thoroughly explores various strategies for effective small dataset learning in ML, examining both existing approaches and introducing novel techniques. The research pivots around two key questions: firstly, it investigates current methods employed for learning from small datasets in machine learning, and secondly, it assesses the efficacy of batch normalization in enhancing model performance and utilizing salient image segmentation as an augmentation policy in self-supervised learning.The thesis comprehensively reviews techniques for managing small datasets, in- cluding data selection and preprocessing, ensemble methods, transfer learning, regularization techniques, and synthetic data generation. A critical examination of batch normalization reveals its significant role in improving training time and testing errors for minority classes in highly imbalanced datasets. The study also demonstrates that utilizing salient image segmentation as an augmentation policy in self-supervised learning substantially improves representation learning. This improvement is particularly evident in the context of downstream tasks such as image segmentation, highlighting the effectiveness of this technique in enhancing model performance.In summary, this study contributes to the field of machine learning by exploring strategies for learning from small datasets. It offers a detailed analysis of batch normalization, highlighting its potential in improving performance for minority classes in imbalanced datasets. Additionally, the study introduces salient image segmentation as an augmentation policy in self-supervised learning, showing its effectiveness in tasks like image segmentation. These findings provide a solid foundation for further research in small sample learning and present practical insights for machine learning practitioners working with limited data. Show less
The research aims to explore the evolutionary adaptability of enzymes and the impact of temperature on protein evolution pathways, using M. tuberculosis β-lactamase BlaC as the object of study.... Show moreThe research aims to explore the evolutionary adaptability of enzymes and the impact of temperature on protein evolution pathways, using M. tuberculosis β-lactamase BlaC as the object of study. Enzymes inherently embody a delicate balance between activity and stability, and the acquisition of new enzymatic functions is often accompanied by trade-offs, such as decreased stability or reduction of the original activity. Probing evolutionary adaptability of BlaC with laboratory evolution in combination with structural characterization can provide information about the mechanisms of rapid adaptations observed for β-lactamases in the clinic. The role of temperature as a conventional selection pressure in such evolutionary adaptation is unclear. The cooperative nature of enzyme unfolding over a narrow temperature trajectory raises the question whether evolution at temperatures well below the melting point is influenced by temperature. The approach used in this work to answer these questions is by simulating evolution under different selection pressures and characterize the variant enzymes in terms of activity, structure, dynamics and melting temperature. The research makes clear how enzyme kinetics and dynamics vary with different selection pressures and maps the evolutionary path that enzymes may take. The underlying structural mechanisms are established to provide a rationale for the observed effects. Show less
The research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency,... Show moreThe research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency, which is evaluated based on donors' hemoglobin and ferritin levels. If either of these levels are inadequate, donors are deferred from donation. Deferral due to low hemoglobin levels occurs on-site, meaning that donors have already traveled to the blood bank and then have to return home without donating, which is demotivating for the donor and inefficient for the blood bank. A large part of this dissertation therefore has the objective to develop a prediction model for donors' hemoglobin levels, based on historical measurements and donor characteristics.The prediction model that was developed reduces the deferral rate by approximately 60\% (from 3\% to 1\% for women, and from 1\% to 0.4\% for men), showing the potential of using data to enhance blood bank policy efficiency. Additionally, the model predictions were made explainable, providing the blood bank with insights into why specific predictions are made. These insights increase our understanding of the relationships between donor characteristics and hemoglobin levels. If this prediction model would be implemented in practice, the explanations could also be shared with the donor to help them understand why they are (not) invited to donate, which could also contribute to donor satisfaction and retention.In a collaborative effort with blood banks in Australia, Belgium, Finland and South Africa, the same prediction model was applied on data from each blood bank. Despite differences in blood bank policies and donor demographics, the models found similar associations with the predictor variables in all countries. Differences in performance could mostly be attributed to differences in deferral rates, with blood banks with higher deferral rates obtaining higher model accuracy.Beyond hemoglobin prediction models, additional research questions are explored. One study aims to identify determinants of ferritin levels in donors through repeated measurements, and linking these to environmental variables. Another study involves modeling the pharmacokinetics of antibodies in COVID-19 recovered donors, and finding relationships between patient characteristics, symptoms, and antibody levels over time.In summary, the research in this dissertation shows the potential within the wealth of data collected by blood banks. The proposed data-driven donation strategies not only decrease deferral rates but also increase donor retention and understanding. This comprehensive approach allows Sanquin to provide more personalised feedback to donors regarding their iron status, ultimately optimising the blood donation process and contributing to the overall efficacy of blood banking systems. Show less
This work describes the use of click-to-release chemistry to get spatiotemporal control over immunocytokine activity. Until now, immunocytokines (cytokines coupled to a tumor-targeting-moiety)... Show moreThis work describes the use of click-to-release chemistry to get spatiotemporal control over immunocytokine activity. Until now, immunocytokines (cytokines coupled to a tumor-targeting-moiety) remained active throughout the body, being able to bind their respective receptors, causing mild to severe side-effects in cancer patients. Attempts have been made to improve the specific action of these immunocytokines, but these solutions remained very cytokine-specific and toxicity was not reduced significantly. Click-to-release chemistry allows us to inactivate a cytokine by blocking its free amines, present in lysines. This prevents the cytokine, IL-1β and TNF-α in particular, from binding its receptor. Removal of the blocking agent using a tetrazine restores the native amine and for IL-1β also its activity. By coupling the blocked cytokine to a targeting moiety allows for transport to the target, the tumor(-environment) upon which the unblocking or decaging can take place. This blocking-unblocking or caging-decaging was assessed using various cell-based assay. This technique can provide new opportunities in the immunocytokine field, as it is not cytokine-specific, and thereby opportunities in cancer therapy development. Show less
The haplolepideous mosses (Dicranidae) comprise about 4000 species distributed over a wide range of habitats, with great gametophytic and sporophytic morphological variation. Their monophyly is... Show moreThe haplolepideous mosses (Dicranidae) comprise about 4000 species distributed over a wide range of habitats, with great gametophytic and sporophytic morphological variation. Their monophyly is well supported by the results of several molecular phylogenetic studies, which shed light on their relationships and circumscriptions, and thus also contributed to identify many remaining problems. Dicranidae ordinal classification is not congruent with current relationship hypotheses and is not supported by morphology. Morphological circumscriptions of some families do not correspond to monophyletic groups. Furthermore, the monophyly of many families and genera with weak morphological circumscriptions remains to be tested. In this thesis, systematics and relationships of the leucobryoid mosses and some families and genera segregated from the former Dicranaceae s.l. were studied using molecular phylogenetic methods. 37 out of the 38 haplolepideous moss families were represented by markers from the three genomes (nrITS, nad5, trnS-trnF, atpB-rbcL). Phylogenetic reconstructions were based on maximum parsimony, maximum likelihood, and Bayesian inference. Ancestral state reconstructions, phylogenetic network analysis (NeighborNet), and relationship hypothesis testing (Shimodaira-Hasegawa test) were performed to contribute to the interpretation of the results of the phylogenetic reconstructions. Morphological circumscriptions were evaluated and improved whenever possible, in line with the results of all analyses performed. Show less
The plant hormone auxin regulates plant growth and development through polar cell-to-cell transport-generated maxima and minima. PIN FORMED (PIN) auxin efflux carriers determine the direction of... Show moreThe plant hormone auxin regulates plant growth and development through polar cell-to-cell transport-generated maxima and minima. PIN FORMED (PIN) auxin efflux carriers determine the direction of this auxin flow through their asymmetric placement on the plasma membrane (PM). In Arabidopsis thaliana (Arabidopsis), the PM-associated protein kinase PINOID (PID) regulates PIN localisation and thereby auxin transport polarity by phosphorylating these carriers in their central cytoplasmic loop. PID in turn interacts with the calmodulin-like protein TOUCH3 (TCH3) in response to elevated cytosolic calcium, which dissociates PID from the PM and inhibits its kinase activity. In this thesis, we show that PID also interacts with 10 other CALMODULIN/CALMODULIN-LIKE proteins, which together with TCH3 form a confined clade in the CaM/CML family. The CaM/CML binding domain in PID was found to map to an amphipathic alpha-helix inserted in the catalytic kinase core. Disruption of this alpha-helix did not affect PID kinase activity, but impaired both its PM association and the interaction with the CaM/CMLs, making the kinase “untouchable”. Expression of “untouchable” PID versions in the pid mutant background revealed that proper calcium-CaM/CML-PID signalling is essential to maintain the robust spiral phyllotaxis that is typical for the Arabidopsis inflorescence. Show less
Biological cells, the basic building blocks of all life forms, are surrounded by a lipid membrane. More than half of the membrane is occupied by membrane proteins, which can regulate the cell... Show moreBiological cells, the basic building blocks of all life forms, are surrounded by a lipid membrane. More than half of the membrane is occupied by membrane proteins, which can regulate the cell functionality through specific arrangements. To regulate the arrangements several proteins have to work together. In addition to direct forces, there exists an indirect force between the proteins, which stems from their deformation of the membrane and contributes to their self-organization. Since the actual membrane is very crowded and proteins are too tiny and complex to measure this interaction, in this thesis we used a model system consisting of lipid membranes and solid particles to study the deformation-mediated interaction. We experimentally confirmed for the first time that, unlike many known forces, this deformation-mediated interaction is not additive, i.e. the strength and range of three (or more) deformations cannot be obtained by simple addition of the interactions between pairs of deformations. We found that the interaction weakens with increasing number of membrane-deforming particles and that the particle become less ordered. We investigated deformations in both directions of the membrane and found that the interaction can be both repulsive and attractive, and furthermore depends on the shape of the deformation. This thesis helps to better understand the organization of proteins that deform cellular membranes. Show less
The emergence of complex diseases resulting from abnormal cell-cell signaling and the spread of infectious diseases caused by pathogens are significant threats to humanity. Unraveling the dynamic... Show moreThe emergence of complex diseases resulting from abnormal cell-cell signaling and the spread of infectious diseases caused by pathogens are significant threats to humanity. Unraveling the dynamic mechanisms underlying cell-cell signaling and infectious disease spreading is crucial for effective disease prevention and treatment. As science and technology advance, the availability and diversity of observational and experimental data related to these biological processes continue to grow. In this thesis, we integrate multisource data with dynamic modeling to investigate the biological mechanisms of Notch signaling in biological development and to develop prevention and control strategies for infectious diseases. Show less
Given the accelerating appearance of antimicrobial resistance, there is an urgent need for more fundamental research into novel antibiotic strategies. The work in this thesis helps to address this... Show moreGiven the accelerating appearance of antimicrobial resistance, there is an urgent need for more fundamental research into novel antibiotic strategies. The work in this thesis helps to address this global problem by developing new antibiotic compounds, inspired by the antibacterial mechanisms of the natural antibiotic bacitracin. By unravelling the unique mechanism of action that bacitracin employs, we discovered that the inclusion of a small hydrophobic group in key locations of the molecule results in a dramatic enhancement of antibacterial activity, in some cases more than 100 times more potent than bacitracin. Crucially we found that the most potent analogues are particularly active against antibiotic-resistant bacteria including those bearing clinically challenging resistance genes. In doing so we have developed potent next-generation variants of this classic antibiotic and have taken important steps in the fight against antimicrobial resistance. Show less
Detecting nanoscopic objects plays an important role in nanoscience in particular, in the rapidly growing field of nanobiology. The forebear to modern super-resolution microscopy for single... Show moreDetecting nanoscopic objects plays an important role in nanoscience in particular, in the rapidly growing field of nanobiology. The forebear to modern super-resolution microscopy for single molecule investigation, is fluorescence microscopy. Fluorescence as a contrast mechanism, however, brings several restrictions. These include (1) the use of the label itself, which may introduce artifacts to the interpretation, (2) the limited photoemission caused by photobleaching and photoblinking as well as (3) low bandwidth of the emission. Fluorescence-free alternatives are thus highly desirable to overcome these limitations. Optical detection of individual proteins with high bandwidth holds great promise for understanding important biological processes on the nanoscale. In this thesis, we investigate label-free optoplasmonic detection of single proteins and particles in motion. Analysing the data provide information about the hydrodynamic volume of the diffuser and interaction such as binding events. Show less
