The endocannabinoid receptors CB1R and CB2R are involved in a plethora of processes, and consequently are involved in many pathological conditions. Their wide distribution makes the CBRs both an... Show moreThe endocannabinoid receptors CB1R and CB2R are involved in a plethora of processes, and consequently are involved in many pathological conditions. Their wide distribution makes the CBRs both an interesting therapeutic target and hard to study. Additional chemical tools are required to study and understand the function and mechanism of CB1R and CB2R. This thesis describes the development of several such tools to improve our insight in the (pathological) roles of the receptors in order to develop novel and improved therapeutics. First evaluation of three dimensional ligand-CB2R complexes made and analysed with Cryo-EM are described. Hotspots that potentially generate selectivity between CB1R and CB2R are evaluated with point-mutations in vitro. Consequently describes the development of the first tools, two-step bifunctional probes based on LEI-121 and LEI 102, is described. As two-step probes are not compatible with every assay, the toolbox is expanded with a one-step fluorescent probe. Briefly touching upon CB1R, ligands were designed with negatively charged phosphonium groups that are potentially selective for mtCB1R. 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
Metabolomics has the potential to play a pivotal role in understanding disease onset and progression, and ultimately personalized treatments. One of its major challenges is its large-scale... Show moreMetabolomics has the potential to play a pivotal role in understanding disease onset and progression, and ultimately personalized treatments. One of its major challenges is its large-scale implementation, which is necessary to deal with the high variability of the metabolome. In this work we have developed tools for automated sample handling and preparation for metabolomics analysis, and bioanalysis in general. The tools are versatile, suitable for high-throughput, and able to deal with sensitive and biomass-limited samples. Sample transfer through segmented-flow can accommodate a wide range of samples and volumes, and can work seamlessly with many downstream processing or analysis. Two sample preparation tools based on droplets; one universal preconcentration tools using controlled evaporation, and one based on simultaneous extraction and enrichment, also provide a versatile interface and can be used to bridge gaps between processing steps. The working principles of these sample handling and preparation tools are universal and can be adapted for specific applications. 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
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 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
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
In recent decades, climate change has led to more frequent and severe drought events, causing serious consequences such as increased forest mortality and significant crop yield losses.... Show moreIn recent decades, climate change has led to more frequent and severe drought events, causing serious consequences such as increased forest mortality and significant crop yield losses. Understanding how drought affects plants, especially economically important herbaceous species, is crucial for predicting and developing drought-resistant crops. To address this issue, this study analyzed a comprehensive dataset of anatomical and hydraulic traits in different genotypes of Arabidopsis thaliana and tomato, including both wild-type and transgenic mutants. The study also investigated the expression of four well-known drought marker genes associated with ABA-dependent and ABA-independent pathways and the impact of overexpressing the JUNGBRUNNEN1 (JUB1) gene on drought response. The findings revealed that each genotype had a unique set of traits to cope with drought, which could be categorized into two response strategies. One group enhanced their drought resistance through traits like a more negative stem P50, thicker intervessel pit membranes, a more lignified inflorescence stem, and a gradual reduction of the low initial stomatal conductance during drought. This strategy enabled them to maintain a relatively high and stable leaf water potential (Ψl). The second group, represented by JUB1 overexpression genotypes, relied primarily on maintaining a high Ψl which is possibly due to osmoprotectant accumulation in leaves, while the other traits have not been recorded. Overall, this research demonstrated the adaptive capabilities of herbaceous plants to drought conditions, highlighting the intraspecific variation in drought responses that underscores the need for a detailed assessment of drought-responsive traits to improve crop yield in a warming world. Show less
Novel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel... Show moreNovel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel entities, leading to the formation of aggregated contamination. In this dissertation, we utilized advanced computational methods, such as molecular simulation, data mining, machine learning, and quantitative structure-activity relationship modeling. These methods were used to investigate the mechanisms of interaction between MNPs and other novel entities, the joint toxic action of MNPs and other novel entities, the factors affecting their joint toxicity to ecological species, as well as to quantitatively predict the interaction forces between MNPs and other novel entities, and the toxicity of their mixtures. The results indicate that understanding the mechanisms of interactions between novel entities and their modes of joint toxic action can provide an important theoretical basis for establishing effective risk assessment procedures to mitigate the effects of novel entities on ecosystems and human health. Furthermore, this dissertation provides important technical support and a practical basis for the quantitative prediction of the environmental behavior and toxicological effects of novel entities and their mixtures by applying various advanced in silico methods individually or in combination. Show less
On the largest scale, the Universe resembles a cosmic spiderweb. Most galaxies coexist in small groups within the threads of this web. At the nodes of the threads are enormous groups of galaxies... Show moreOn the largest scale, the Universe resembles a cosmic spiderweb. Most galaxies coexist in small groups within the threads of this web. At the nodes of the threads are enormous groups of galaxies forming the largest structures in the universe still held together by gravity: clusters of galaxies.Clusters of galaxies consist of thousands of galaxies, although the galaxies constitute only a few per cent of the total cluster mass. The majority of the (non-dark) mass of a cluster is in a hot and dilute gas that resides in the space between galaxies and is permeated by magnetic fields. Clusters grow by collisions with other clusters, shocking and heating the gas causing amplification of magnetic fields and acceleration of particles to near the speed of light. This makes clusters a source of radio synchrotron radiation.This thesis investigates the particle acceleration process and the magnetic fields of merging clusters using the LOFAR and VLA radio telescopes. The thesis presents, among other things, one of the few radio maps of clusters at ultra-low frequencies and examines clusters of lower mass than usual. Additionally, the thesis includes observations of a sample of over a hundred clusters to statistically determine the properties of the magnetic field in clusters in a novel way. Show less
Atherosclerosis is a progressive disease resulting in the formation of an arterial plaque. Despite lipid lowering, recurrent cardiovascular events remain a risk. While atherosclerosis is primarily... Show moreAtherosclerosis is a progressive disease resulting in the formation of an arterial plaque. Despite lipid lowering, recurrent cardiovascular events remain a risk. While atherosclerosis is primarily lipid-driven, the immune system plays a critical role in the pathophysiology. Additional treatment could be achieved via immunomodulation. We aimed to identify potential biomarkers for monitoring of immunomodulatory drugs in future clinical trials and investigated pharmacological modulation of atherogenic pathways. We identified smokers and elderly healthy people as suitable groups for future clinical trials. We investigated the impact of sample aging on LPS responses, and optimized methodology for evaluation of LPS-driven neutrophil responses, in vitro and in vivo. As potential anti-atherogenic strategy, we evaluated the effect of pneumococcal vaccination on circulating oxLDL-IgM levels in man. The immunomodulatory impact of hydroxychloroquine, a drug with potential anti-atherogenic effects, was evaluated in healthy volunteers. A novel OX40L inhibitor was tested in healthy volunteers, since the OX40-OX40L axis may play a role in atherogenesis. OX40L inhibition was safe and effectively reduced T cell activity. Lastly, we showed that PD-1 agonism reduced atherosclerosis in Ldlr-/- mice. This thesis adds to the future development of effective and specific immunomodulatory treatments for atherosclerosis. Show less
To contribute to the body of knowledge aiming at a better coverage of ecosystem service assessment in LCA studies, this thesis dives into the challenges of incorporating existing ecosystem service... Show moreTo contribute to the body of knowledge aiming at a better coverage of ecosystem service assessment in LCA studies, this thesis dives into the challenges of incorporating existing ecosystem service methods within the impact assessment phase of the conventional LCA framework. Through this thesis, we present an overview of ecosystem service categories that could represent an optimal coverage for their inclusion in LCA, and provide a clear example on how to overcome the challenges of characterizing key environmental impacts that are otherwise missing or misrepresented in LCA results and that influence the quality and supply of ecosystem services. We demonstrate the approach proposed with the development of readily applicable CFs that will allow future LCA studies to account for land use impacts on pollinator abundance, and provide further evidence on the benefits of interdisciplinary collaboration as a way to strengthen our capacity to estimate anthropogenic impacts, with the use of expert elicitation methods as a valuable tool to fill in key data gaps. Lastly, we recommend to continue efforts towards an overarching archetype classification that can facilitate the inclusion of multiple biogeographical and socio-economic factors for the identification of representative patterns, and provide input across multiple impact categories at relevant spatial scales. Show less
β-Lactamases are enzymes that can break down β-lactam substrates, such as antibiotics, preventing the use of these antibiotics for the treatment of various infectious diseases. However, some... Show moreβ-Lactamases are enzymes that can break down β-lactam substrates, such as antibiotics, preventing the use of these antibiotics for the treatment of various infectious diseases. However, some compounds, β-lactamase inhibitors, can block these enzymes allowing for possible treatments using a combination of antibiotic and inhibitor. BlaC is the β-lactamase of Mycobacterium tuberculosis, the bacteria that cause tuberculosis, and is used as a model for protein evolution. To understand if and how BlaC can develop resistance against certain inhibitors we studied the evolutionary adaptability of this enzyme. We used laboratory evolution and various biochemical techniques to characterize several mutations in BlaC and subsequently tested the effect of combining mutations. One of the findings is that BlaC can easily become less sensitive to the inhibitor sulbactam by partially blocking the entrance to the active site. Interestingly, this was accompanied by increased sensitivity to another inhibitor, avibactam, that could not be compensated for by other mutations.Generally, Escherichia coli bacteria are used to test the effects of BlaC variants in cells, as they are easy and safe to use in the lab. We show that results obtained for E. coli can be extrapolated to conditions that resemble tuberculosis disease in humans: the M. marinum infection model of zebrafish. All these findings are of interest for the future development of combination therapies to treat tuberculosis. Show less
With the rapidly growing number of extrasolar planets detected, we have firmly stepped into the era of detailed characterization. Diverse types of exoplanets such as gas giants on close-in orbits ... Show moreWith the rapidly growing number of extrasolar planets detected, we have firmly stepped into the era of detailed characterization. Diverse types of exoplanets such as gas giants on close-in orbits (hot Jupiters) and young massive giants on wide orbits (super Jupiters), with no analogs in the Solar System, pose challenges but also opportunities to our understanding of planet formation and evolution. Exoplanet atmospheres with imprints from their history open an important avenue to retrace the origin and evolution of planets. With high-dispersion spectroscopy, we can resolve atomic and molecular spectral features into unique forests of lines that serve as the fingerprints for identifying different species in planetary atmospheres. In this dissertation, I utilize this technique to explore atmospheric compositions, thermal structures, and dynamics of exoplanet atmospheres. I have discovered minor isotopologues in emission spectra of an exoplanet and a brown dwarf for the first time, pioneering the use of carbon isotopic ratios as potential tracers of planet formation. I have investigated the trend of atomic absorption strengths in a sample of ultra-hot Jupiters, which enables disentangling different dynamic regimes of highly-irradiated exoplanets. These works form the foundation to link spectroscopic observations to planet formation and evolution processes. Show less
