Search

Bispecific antibodies (BsAbs) are a class of monoclonal antibodies that can target two different antigens/epitopes simultaneously. BsAbs can be roughly divided into two classes: IgG-like BsAbs and fragment-based BsAbs. Fragment-based BsAbs have a much shorter serum half-life due to the absence of the Fc fragment, which has limited their application in the clinic. In contrast, IgG-like BsAbs have relatively long serum half-lives, however, generating pure IgG-like BsAbs and improving their yield is still challenging due to chain association issues. The production of a BsAb in one expression cell line is very difficult and unfavourable due to challenges in extracting the desired BsAb from the lysates and the inherently low yield (Chapter 1, Figure 2). Thus, the aim of the research described in chapter 3 was to design a novel format to improve the purity and yield of BsAb during production. We have combined a conventional antigen-binding fragment with a single-domain antibody and generated BsAbs in a Fab x sdAb-Fc format. Our data show that this format can avoid potential heavy-light chain mis-pairing during the production of BsAbs, and increases the purity of BsAbs to above 95%. Further characterization assays showed that the BsAbs in this configuration nevertheless maintained the ability of binding to two distinct antigens concurrently. During the design of this novel BsAb format, we found that the hinge region can be adapted for different application scenarios. There are already several studies indicating that enhanced T-cell mediated tumor cell elimination can be achieved by decreasing the distance between T cells and tumor cells. In our case, the distance between tumor cells and T cells can be modulated by different hinge designs. Thus, we modulated the hinge region in T-cell redirecting bispecific antibodies (TRBAs) and studied their anti-tumor activity in in vitro assays. Our data show that with less space separating the two arms of the TRBA, tumor cells and effector T cells can bridge more tightly, which strengthens T cell activation and, in turn, increases tumor cell death. Therefore, our data indicate that the modulation of the ‘hinge region length’ parameter can possibly contribute to future design of similar molecules (Chapter 4). Since the antibody Fc-tail activates specific immune effector mechanisms, antibody isotype plays an important role in cancer therapy. Previous studies in a mouse tumor model showed the efficacy of prophylactic application of mIgG2a isotype antibodies. However, human cancer patients usually receive antibodies in a therapeutic setting. Thus, in the last study, we developed a panel of anti-Thy1.1 antibodies with various isotypes (mIgG1, mIgG2a, mIgE and Fc-silenced) and evaluated their effectiveness in a therapeutic setting in mouse tumor models. Our data demonstrated that mIgG2a is the most effective isotype in treating cancer in a therapeutic setting in mice. Therefore, isotype selection is a critical parameter determining the efficacy of tumor-targeting antibody therapy. We believe future research in tumor immunotherapy may benefit from the knowledge we gained in designing tumor antigen targeting antibodies for cancer therapy (Chapter 5).

LITURGICAL VESTMENTS IN REPRESSION Paraments in the Dutch Republic as Bearers of Identity, 1580-1650 Liturgical vestments are the most striking and most valuable components of the material culture of the Catholic Church. The Reformation that was implemented in the Northern Netherlands around 1580 and the subsequent innovations prescribed by Rome had major consequences for these treasures. During this period, the Catholics in the Dutch Republic were driven to the margins of society. They were initially forced to congregate in private houses. It was not until the course of the seventeenth century that hidden churches were created. Nevertheless, as early as the sixteenth century, a start was made on reassembling a church inventory and a basic wardrobe with salvaged items. At the same time, new liturgical vestments were made as well. How were design and symbolism applied in the liturgical vestments to confirm the past of the church (continuity) on the one hand and to renew the church from within (discontinuity) on the other? In this interdisciplinary research, the unique collection of church vestments of Museum Catharijneconvent and the collections of several Old Catholic churches in our country form the research material. Combined with archival research, literature study and research in contemporary sources, a new story about identity and oppression emerges.

Type systems (in the form of categorial grammars) are the front runners in the quest for a formally elegant, computationally attractive and flexible theory of linguistic form and meaning. Words enact typed constants, and interact with one another via means of grammatical rules enacted by type inferences, composing larger phrases in the process. The end result is at the same time a parse, a proof and a program, bridging the seemingly disparate fields of linguistics, formal logics and computer science. The transition from form to meaning is traditionally handled via a series of homomorphisms that simplify nuances of the syntactic calculus to move towards a uniform semantic calculus. Alluring as this might be, it poses pragmatic considerations. For the setup to work on the semantic level, one has no choice but to start from the hardest part, namely natural language syntax. Phenomena like movement, word-order variation, discontinuities, etc. require careful treatment that needs to be both general enough to encompass the full range of grammatical utterances, yet strict enough to ward off ungrammatical derivations. This thesis takes an operational shortcut in targeting a ``deeper'' calculus of grammatical composition, engaging only minimally with surface syntax. Where previously functional functional syntactic types would be position-conscious, requiring their arguments in predetermined positions upon a binary tree, they are now agnostic to both tree structure and sequential order, alleviating the need for syntactic refinements. This simplification comes at the cost of a misalignment between provability and grammaticality: the laxer semantic calculus permits more proofs than linguistically desired. To circumvent this underspecification, the thesis takes a step away from the established norm, proposing the incorporation of unary type operators extending the function-argument axis with grammatical role labels. The new calculus produces mixed unary/n-ary trees, each unary tree denoting a dependency domain, and each n-ary tree denoting the phrases which together form that domain. Although still underspecified, these structures now subsume non-projective labeled dependency trees. More than that, they have their roots set firmly in type theory, allowing meaningful semantic interpretation. On more practical grounds and in order to investigate the formalism's adequacy, an extraction algorithm is employed to convert syntactic analyses of sentences (represented as dependency graphs) into proofs of the target logic. This gives rise to a large proofbank, a collection of sentences paired with tectogrammatic theorems and their corresponding programs, and an elaborate type lexicon, providing type assignments to one million lexical tokens within a linguistic context. The proofbank and the lexicon find use as training data in the design of a neurosymbolic proof search system, able to efficiently navigate the logic's theorem space. The system consists of three components. Component one is a supertagger responsible for assigning a type to each word — the tagger is formulated as a heterogeneous graph convolution kernel that boasts state-of-the-art accuracy. Rather than produce asignments in the form of conditional probabilities over a predefined vocabulary, it instead constructs types dynamicaly. As such, it is unconstrained by data sparsity, generalizing well to rare assignments and producing correct assignments for types never seen during training. Component two is a neural permutation module that exploits the linearity constraint of the logic in order to simplify proof search as optimal transport learning, associating resources (conditional validities) to the processes that require them (conditions). This allows for a parallel and easily optimizable implementation, unobstructed by the structure manipulation found in conventional parsers. Component three is the type system itself, responsible for navigating the produced structures and asserting their well-formedness. Results suggest performance superior to established baselines across categorial formalisms, despite the ambiguity inherent to the logic.

In this PhD thesis, fluorescent molecules or nanoparticles were investigated to probe the pore space of heterogeneous catalysts and mass transport therein. The fluorescent emitters (guests) report on the properties of the catalyst (hosts) via guest–host interactions. These interactions can be a resistance on the movement of the probes by the pore space, reversible and irreversible adsorption of probes on the pore wall, or a chemical reaction between the guest and host. It is advantageous to follow reporters via their fluorescence because it can be detected with high sensitivity, e.g., to determine the location of the reporter, which makes it possible to probe individual molecules or nanoparticles. A second advantage is that the fluorescence emission spectrum is often dependent on the chemical identity of the fluorescent reporter. Thus, chemical reactions of the guest with the host can be followed directly via the guest’s emission spectrum. Analysis of the trajectories obtained with single-molecule tracking in inorganic porous hosts is often challenging because trajectories are short and/or motion is heterogeneous. In Chapter 3, we presented the software DiffusionLab for motion analysis of such challenging datasets. This approach relies on the pooling of trajectories with a similar motion behaviour into a population. The average motion characteristics of a population can be computed and compared without losing information on the single-molecule level about, e.g., the spatial distribution of adsorption. In Chapter 4, we introduced a microfluidic device designed for the characterization of fluorescent reporters in confinement. The device consists of a two-dimensional model pore with a height of 50 nm. This design allowed for the measurement of long trajectories, which facilitated detailed probe characterization. We investigated guest–host adsorption of single quantum-dot emitters to the pore wall and guest diffusion through the model pore. Building on the probe characterization, we defined conditions that allowed for mapping of the accessible pore space of a model-pore array and a real-life catalyst particle. We studied molecular diffusion in the straight and the sinusoidal pores of ZSM-5 zeolites in Chapter 5. The pore types have a similar effective pore size but a different shape. Both the mobility, when the molecules were not adsorbed over hundreds of milliseconds, and the frequency of adsorption was different in the straight and sinusoidal pores. Additional meso- and macropores with a diameter ≥ 2 nm were etched into the zeolite material to promote diffusion. The molecular mobility increased and the adsorption frequency of the molecules decreased in the sinusoidal pores, which indicated the formation of an interconnected secondary pore network. In Chapter 6, we studied the guest–host interactions of fluorescent resorufin molecules inside the micropores of ~10 micrometre-sized zeolite-β crystals. We found that resorufin molecules were protonated after entering the zeolite, which provided contrast between the resorufin species inside the zeolite micropores and in aqueous solution. Using this effect, it was shown that the micropores in the crystals were fully accessible. Moreover, we visualised that diffusion through the straight pores of zeolite-β was impeded when diffusing through the boundaries between the zeolite subunits.

This thesis aims to act as a catalyst for research at the intersection of energy transition and industrial transformation by providing a modelling toolbox suited for the application to industrial energy systems. The modelling work in this thesis is based on mixed-integer linear programming. It uses a framework designed to optimise multi-energy systems at different scales for cost and emissions using hourly resolved data. It does so by deciding the installation, size, and operation of a set of energy conversion and storage technologies to supply specific energy demands. The modelling toolbox's contributions can be grouped into two categories: technology models compatible with the optimisation framework and methodological improvements. For the first category, this work presents a linear model of a monoethanolamine-based post-combustion carbon capture process that considers the effects of CO2 concentration, plant size, and part-load operation. Moreover, a gas turbine model was developed at different levels of detail to suit the user's needs and preferences. For the second category, a novel solution method for time-resolved energy-system optimisation problems was derived, called the time-hierarchical solution method. Besides the obvious advantage of saving computation time, the method's added value lies in its simplicity and suitability for energy systems with a high penetration of non-dispatchable renewable energy sources. Especially the latter is a feature that most solution methods are lacking. Furthermore, this thesis also presents a method to deal with large portfolios of existing wind turbines. While this is not required for greenfield designs, it is crucial when the existing infrastructure is considered and replacement decisions are optimised. This thesis also studied the role of hydrogen and carbon capture using two extensive case studies. Hydrogen was studied on a national level, and the effect of industrial hydrogen demands on the Dutch national energy system was assessed. It was found that the role of hydrogen as a storage medium for electricity decreases with increasing (industrial) demand. Beyond a certain tipping point, the cost of hydrogen increases due to increasingly costly system expansion. This finding puts the notion of cheap green hydrogen in the future - at least if produced nationally – into question. The role of carbon capture was studied for the decarbonisation of the steel industry in combination with process changes. In the long term, carbon capture will not be necessary but beneficial, nevertheless. In the short term, however, carbon capture can significantly boost the CO2 reduction at a cost that is compensated by the savings in costs of emitting CO2. The study also shows that the risk of technology lock-in can be mitigated if the system is properly designed with a forward-looking perspective. To summarise, this thesis provides (i) the technology models to simulate and optimise industrial energy systems, (ii) the methodologies to do so in a computationally efficient manner, (iii) exemplary case studies with a detailed description of the methodology as a guideline for other case studies, and (iv) insights into the role of hydrogen and carbon capture.

Many primary school students in the Netherlands struggle with reading comprehension, especially when it comes to higher-order comprehension skills, such as summarizing. Scientific literature has suggested various ways to foster text comprehension, for example by providing explicit instruction about text structures (e.g., cause-effect, chronology), as this might foster the construction of a solid mental representation of the text: a coherent situation model (e.g., see Meyer & Ray, 2011). A text structure is the underlying skeleton of a text that organizes all the information. Text structure can be defined as the organization of ideas, the relationship among the ideas and the vocabulary used to convey meaning to the reader” (Pyle et al., 2017:1; see also Armbruster, 2004). What if we would teach children in primary school about text structures? Our meta-analysis showed that explicit instruction about narrative and informative text structures yields positive immediate effects on students' reading comprehension, and that effects vary across outcome measures: recall tasks (g = 0.37), summarization (g = 0.57), reading comprehension tests (g = 0.25), and text structure knowledge tests (g = 0.38) for students in the upper elementary grades. The moderator analysis revealed that content-related features such as a focus on paragraph-level structure, active construction of graphic organizers, and rule-based summarization techniques moderated the effectiveness, but these effects also varied with outcome measure. The mixed-methods study examined the state-of-affairs analysis of the current Dutch primary school reading curriculum for grades 4 and 5. The data are based on a materials analysis of reading comprehension lessons (n = 80) in eight textbooks for grades 4 and 5, supplemented with semi-structured teacher interviews (n = 29) and lesson observations (n = 11). Main findings were (1) a lack of alignment between lesson goals, theory, and assignments, (2) a strong focus on practicing strategies, (3) limited declarative knowledge about strategies and text structure, (4) little opportunities for selfregulated strategy application, and (5) strong emphasis on individual question answering. Therefore, a lesson series on text structure was developed in a design-based research in close collaboration with four teachers. Based on data from lesson artefacts, logbooks, panel interviews, and lesson observations, this study revealed several difficulties related to the design principles and their implementation in practice, and identified several areas in which teachers' pedagogical content knowledge about reading comprehension could be strenghthened. This lesson series was then tested for its effectiveness among 201 students in a switching-panels replication design with three measurement occasions. There were positive effects on writing skills for all students, but the effects on reading, summarizing, and metacognitive knowledge varied between intervention groups. This variability in effects raised questions about the exact lesson implementation. In the final study, we closely monitored three teachers and their students in another implementation of the intervention. Implementation fidelity scores varied across teachers and lesson phases, with the lowest scores for collaborative learning (51%) and the highest for explicit instruction (91%).This study generated useful insights for the professionalization of teachers and reinforcement of teaching materials for reading comprehension instruction.

‘Remembering Revolutionary Women’ contributes to a growing body of scholarship, positioned at the intersection of cultural memory studies and social movement studies, that shows the importance of memory to activism. Understanding the remembrance of oppositional figures, for whom there appears to be little moral imperative or state agenda to ensure their sustained remembrance, requires a new approach to the workings of cultural memory. I argue that the remembrance of contentious individuals depends on their salience to subsequent social movements and demonstrate how that salience is brought out through an interaction between the work of “mnemonic stakeholders”, on the one hand, and the availability of symbolic resources created during a figure’s lifetime, on the other. This argument is developed through a comparative analysis of the cultural afterlives of Louise Michel (1830–1905), Emma Goldman (1869–1940) and Sylvia Pankhurst (1882–1960): three revolutionaries who were remarkable for their energetic pursuit of many causes and campaigns over an extended period. My sources include biographical works, artistic installations, performances, portraits and archives, with a focus on those longform versions of Michel, Goldman and Pankhurst’s lives in which a prevailing representation is in some way challenged and changed. I make use of methods drawn from cultural memory studies, alongside insights from studies of auto/biography and reputation, to analyse narrative and rhetorical patterns across mediations and remediations of these figures’ lives. Using a model of path-dependency (which shows how the choices available to mnemonic actors in each period are bound by the previous uses to which a figure has been put) I consider variations in how Michel, Goldman and Pankhurst have been remembered along axes of salience, valence and ownership, tracing their territorialisation, de-territorialisation and re-territorialisation in accordance with the needs and desires of later actors and movements. The argument that builds over the course of my chapters gives a new understanding of the hard-won remembrance of figures of resistance. I demonstrate the multi-agential cultural memory work whereby Michel, Goldman and Pankhurst come to be remembered as individuals, as revolutionaries and as women. The cultural afterlives of individual revolutionary women endure because of an encounter between significant representational resources from their lifetimes and the commitment of later memory workers. That memory is only likely to grow further if its salience to a social movement is proved.

The field of Natural Language Processing (NLP) focuses on developing computational techniques to analyze and extract information from human language. With the exponential growth of unstructured textual data, NLP-based techniques have become essential for extracting valuable insights from this data. However, existing information extraction systems have limitations in terms of extracting valuable information without predefined relations or ontology and storing the extracted knowledge effectively. This Ph.D. thesis aims to enhance open information extraction methods to represent unstructured textual data efficiently and effectively. The first part of the research focuses on Open Information Extraction (OIE) systems and their challenges. Existing OIE methods, including pattern-based and machine learning-based approaches, as well as neural techniques, are analyzed to understand their limitations. A Bidirectional Gated Recurrent Unit (Bi-GRU) OIE model is proposed in Chapter 3, which utilizes contextualized word embeddings to extract relevant triples from unstructured text. Experimental results demonstrate the effectiveness of this model in generating high-quality relation triples. Chapter 4 addresses the lack of labeled data, a common problem in NLP tasks. The research extends the OIE model from Chapter 3 by using learned features to generate relation triples and explores the transferability of these features across different OIE domains and the related task of Relation Extraction (RE). The results show comparable performance with traditional training, indicating the potential of OIE in achieving NLP performance without labeled data. In Chapter 5, the focus shifts to enhancing pre-trained language models for taxonomy classification. Pre-trained language models often struggle with unseen patterns during inference, and the limited size of annotated data poses a challenge. A two-stage fine-tuning procedure, incorporating data augmentation techniques, is proposed to improve the generalizability of pre-trained models. Experimental results demonstrate strong generalizability on unseen data, with an F1 score of 91.25%. Chapter 6 explores the use of OIE for constructing a knowledge graph, specifically in the context of cyber threat intelligence. Open-CyKG, an open cyber threat intelligence knowledge graph framework, is designed using an attention-based neural OIE model and a Named Entity Recognition (NER) model. Refinement and canonicalization techniques are employed to overcome ambiguity and data redundancy during knowledge graph construction. The results show that querying the constructed knowledge graph can be done efficiently, highlighting the support of OIE in knowledge graph development. The proposed components achieve beyond-state-of-the-art results in terms of OIE performance, NER performance, and knowledge graph canonicalization. The research presented in the previous chapters demonstrates significant improvements in the efficiency and effectiveness of open information extraction methods for representing unstructured textual data. These advancements leverage techniques such as data augmentation, multi-stage fine-tuning, and pre-trained language models. The construction of knowledge graphs, enabled by OIE, has the potential to mimic human intelligence and benefit various complex applications, including recommender systems, search engines, and dialog systems.

School leaders wishing to advance distributed leadership often end up in what Kessels (2021) calls the leadership paradox. The school leader openly states their wish to stimulate distributed leadership, but displays behaviour that hinders, rather than helps this development. This behaviour comes from underlying tension that school leaders experience with distributed leadership. To “solve” these tensions, school leaders often take leadership into their own hands. As a result of this ‘backlash’ of a more traditional leadership paradigm in which the leader is seen as the ‘hero’ (Liljenberg & Andersson, 2019; Spillane, 2006), school leaders consciously or unconsciously restrict the autonomous space needed for teacher leadership. There are indications in both practice and research that the ‘beliefs’ of school leaders play a role in the tensions which they experience in the transition to distributed leadership (Marchial & Wouters, 2018; Kessels, 2021; Louws et al., 2020; Van der Hilst, 2019). Beliefs are conceptual representations in which knowledge of objects, people, and experiences, as well as their characteristic relationships are saved (Hermans et al., 2008). This dissertation offers insights into how the school leader can become more self-aware, and through this growth in self-awareness may come to a deeper understanding of their own beliefs. Self-awareness can lead to taking a ‘different’ view, to being able to see ‘more’ and finally to more conscious action that brings the ‘desire’ and the ‘doing’ closer together, ensuring that there is less of a leadership paradox. In other words: self-awareness leads to more freedom (of choice) in leadership (Bieri, 2006; Bieri, 2012). In this study distributed leadership is defined as a dynamic process in which teachers temporarily acquire and assign leadership within a factual and perceived autonomous space. Teachers influence their colleagues, the organization, and their surroundings from this position. With distributed leadership, the school leader promotes the autonomous space and the informal, individual and collective leadership of teachers. This definition is in line with the agency movement of distributed leadership which emphasizes that leadership arises organically and voluntarily (DeRue & Ashford, 2010; Gronn, 2002; Harris, 2008; Spillane, 2006; Spillane et al., 2004). School leaders in this research were only partially aware of their own beliefs and the role they played when making choices in their leadership. It can be cautiously stated that school leaders with beliefs mainly in the dynamic belief system and space-creating beliefs, are less likely to experience tension with distributed leadership. These school leaders will be quicker to create the autonomous space for leadership of teachers than school leaders whose beliefs mostly fall in the governing belief system with primarily space-consuming beliefs. This dissertation clearly shows that the school leader can help the autonomous space for leadership of teachers and that they consciously, but mostly unconsciously hinder it.

Sometimes it can be challenging to collect enough data. Think of naturally small populations, such as people with rare diseases. Or hard to access target groups, such as people with addiction problems or undocumented migrants. However, all statistical methods require a certain amount of data to perform well. Recently, more and more often researchers with small samples switch to a Bayesian approach to deal with analysis problems. In the Bayesian framework, observed data is combined with prior knowledge (e.g., knowledge based on opinions of experts in the field or based on previous studies). This prior knowledge is captured in a distribution, the so-called prior distribution. The studies in this dissertation show that the switch to a Bayesian approach is not without problems. Some software programs offer built-in default prior distributions, which are unfortunately not always suitable and can lead to incorrect results when samples are small. The use of Bayesian statistics in that case is not a solution to analyse a small sample. Blindly relying on built-in default priors is not an option when samples are small. The smaller the sample size, the more important the prior distributions. In Chapter 1 of this thesis, the results are presented from an extensive systematic literature review on the performance of Bayesian and frequentist estimation methods under small samples for Structural Equation Models (SEMs). Based on this literature review, we conclude that with small samples the use of Bayesian methods with only default priors can lead to severely biased results. We end the chapter with recommendations for researchers on analysing a small sample size and on how to specify thoughtful prior distributions. In Chapters 2 and 3, we discuss the results of simulation studies. Based on these studies we advise researchers with small samples to specify informative priors. We recommend researchers take the most careful approach possible: start with carefully constructing prior distributions; and assess the impact and robustness of the specified priors through an extensive sensitivity analysis. When researchers are not able or willing to include prior information, we advise to use the twostep method or factor regression score. These methods are a safer choice than maximum likelihood estimation, as those led to higher convergence rates without negative variances, more stable results across replications and less extreme parameter estimates than maximum likelihood estimation with small samples. In the final chapter, we discuss in a non-technical tutorial the risks of using Bayesian estimation while blindly relying on built-in software default priors when samples are small. Also, we demonstrate an online educational Shiny app, in which users can play around with varying sample sizes and prior settings to investigate the impact of priors on the results. In addition, we provide guidelines on how to use Bayesian SEM in a thoughtful way when samples are small.

The purpose of this dissertation was to advance our knowledge on the effectiveness of youth interventions that utilize the social network. To this end, we had three aims: 1) to provide insight into the effectiveness of youth care programs that utilize the social network; 2) to zoom in on the effectiveness of one program specifically, which is developed for youth with mental health needs in multi-problem families (i.e., InConnection); and 3) to better understand the links between pandemic-related stress, informal and formal support and mental health in multi-problem families. 1. How effective are interventions that activate the informal network? An overview study with different intervention types showed that youth interventions which activate the informal network are not more effective than interventions in which the network is not engaged. However, under certain circumstances, interventions that activate the social network were more effective. For example, interventions in which youth decide for themselves who to engage from their informal network, interventions in which only one person is involved, and interventions for youth with mental problems, did have significant effects. 2. How effective is InConnection for youth from multi-problem families? InConnection is an outpatient, systemic treatment for multi-problem families, in which a multidisciplinary team of professionals collaborates with an informal mentor according to the youth-initiated mentoring (YIM) approach. Interviews showed that almost all participants were positive about the collaboration with a YIM. The match between the needs of youth and parents and what mentors thought they could offer turned out to be good in most cases. The effectiveness study showed that InConnection showed some positive effects compared to treatment as usual, namely a decrease in the emotional and behavioral problems of youth and an increase in positive parenting behavior from the parents' perspective. No differences in effectiveness were found for the other outcome measures, including youth well-being and resilience. 3. Does support protect against mental health problems in youth and parents from multi-problem families during the Covid-19 pandemic? Due to the measures taken during the Covid-19 pandemic, youth and their parents had fewer opportunities to receive support from their network, while the risk of mental health problems increased, especially among vulnerable groups. Results showed that the pandemic was experienced as stressful by some youth from multi-problem families. Youth who experienced pandemic-related stress had a higher risk of mental health problems, especially if they reported receiving little or no formal support (therapist support). Parents were minimally affected by pandemic-related stress, suggesting strength and flexibility. Youth and parents who experienced support from their informal and/or formal networks during the pandemic had fewer problems, thus showing the importance of providing help and support to people with mental health problems during stressful situations such as a pandemic. These findings illustrate that despite the complex challenges that vulnerable youth (such as youth with mental health problems and youth from multi-problem families) experience, interventions with characteristics of YIM in general and InConnection specifically are as good or even better than other interventions in supporting youth and reducing their problems.

The binding of a viral pathogen to the host cell is the first, crucial, step of infection. This first step in host-pathogen interactions is dependent on the attachment tools of the viral pathogen and the presentation of attachment factors by the host. Pathogen receptor specificity and host receptor distribution govern which cells and tissues are susceptible, known as host tropism. Using our own developed tissue microarrays to represent the host and the viral attachment proteins to represent the pathogen, we set out to study how receptor-binding interactions can improve our understanding of receptor distribution of both domestic and wild animals and how glycobiology plays a key role in driving these receptor-binding interactions. We used these tools to study and map the receptor binding of influenza A & D attachment proteins in respiratory tissues of various domesticated and wild animals. Receptor binding is the first step in infection and it is therefore of high importance to study it. The use of tissue microarrays is a great in-between system for studying the distribution of these host receptors. The system does have its shortcomings, particularly in terms of glycolipid and mucin detection. To summarize, TMAs are not a replacement for current methods used, such as glycan arrays and animal experiments, but are an excellent addition that complements those systems for studying receptor interactions.

International audience; Colloidal semiconductor nanocrystals (NCs) are believed to strongly absorb resonant light; this property is key to ongoing applications of NCs as phosphor in light-emitting diodes (LEDs) for lighting, in displays, and photodetectors. However, it has been realized that NCs in a low dielectric medium suffer from a weak permeation of the electromagnetic field in the solid due to formation of depolarization fields, strongly reducing light absorption, as quantified by the local-field factor. For low-dimensional semiconductors, such as one-dimensional (1D) rods and two-dimensional (2D) superlattices, this depolarization field depends critically on the electromagnetic field direction versus the long direction of the material. Here we present atomistic calculations of the screening of the electromagnetic field for individual NCs and technologically important architectures, i.e., 1D and 2D superlattices of epitaxially connected NCs. We also investigate the combined effects of epitaxial connections and excitonic interactions in the particular case of dimers of NCs. Even if it can be improved by a matrix of high dielectric constant, the local-field factor of individual NCs is very low but can increase more than an order of magnitude in NC chains or NC superlattices for an in-line or in-plane electromagnetic field. The calculations of the optical absorptance agree with recent experimental results obtained on superlattices of PbSe and CdSe superlattices. The reduced screening of the electric field in the case of strong epitaxial connections between the NCs was recently invoked to explain that the absorptance of a 2D superlattice approaches the absorptance quantum (πα, where α is the fine structure constant) observed with semiconductor quantum wells [P.T. Prins et al., Nano Lett. 21, 9426 (2021)]. We conclude that atomically coherent superlattices have a strong light absorption and emission, even in the NC monolayer limit, and are primary candidates for polarization-selective photodetectors and LEDs.

Summary Infants born very preterm - before 32 weeks gestation and ≤1500 grams birthweight - are at risk of developmental problems. Gross motor development is an early marker in the first year of life to identify developmental delay. As in other developmental domains, gross motor development is characterized by variability between infants known to be related to both infant and environmental factors. This variability makes predicting infant motor development difficult and with that clinical decision-making on initiating early intervention. Repeatedly, within the same child, measuring gross motor development over time makes discrimination of typical or atypical development and developmental progress possible. The Alberta Infant Motor Scale (AIMS) is an observational instrument frequently used by paediatric physiotherapists worldwide to measure infant gross motor development. Among other things, this dissertation describes cross-cultural research to examine the validity of the Canadian norm values of the AIMS for the Dutch population of infants from birth to independent walking. Next, a longitudinal study was conducted to explore the variation in course and speed of gross motor development of Dutch very preterm infants until independent walking and influencing factors. Parents made multiple home video recordings of their infant (corrected age 3.5 to 17.5 months), from which gross motor development was assessed with AIMS. Lastly, parental beliefs about their child's motor development and experiences with the home-video method were examined. Main outcomes of the studies are 1) Dutch infants develop at a slower pace than Canadian infants from birth until independent walking, 2) there is strong evidence for the association of birthweight and moderate evidence for gestational age and sleeping position with infant gross motor development, 3) gross motor growth curves showed that three profiles, comparable to profiles observed among term born infants, can be distinguished: early developers, gradual developers and late bloomers and, 4) parents of very preterm infants believed stimulating motor development to be more important than parents of term born infants.

Extracellular vesicles (EVs) are cell-derived multicomponent messengers that participate in diverse intercellular communication processes regulating immune responses, cell survival and proliferation. However, EVs also play a role in disease processes like cancer. Since EVs contain biological information that reflects the status of the cell of origin, they might be used in liquid biopsies as disease biomarkers. The detection and characterization of EVs is a fundamental step towards EV-based biomarker profiling of pathological processes and is complicated due to their small size, heterogeneity and complex composition. Therefore, technological solutions that allow high throughput detection and analysis of single EVs are of utmost importance. Flow cytometry (FCM) is a promising technology for this purpose. However, traditional flow cytometers, designed for cells, lack the sensitivity for the detection of nano-sized EVs, whose signals are typically below or at the detection limit of conventional instruments. In my PhD thesis I explored the limits of flow cytometry of single EVs, provide guidance for reproducible and robust EV FCM and address the challenges of EV analysis in body fluids. First, we developed and characterized a reference material for EV research. Next, we focused on high-sensitivity FCM for the detection of EVs and I present a reporting framework developed by the ISEV-ISAC-ISTH EV Flow cytometry workgroup to increase reproducibility of results. Since fluorescence calibration of EV signals is of importance for inter-instrument and inter-lab comparisons, we provide insights into the utilization of current calibrators and show that intrinsic variations related to these calibrators compromise accurate calibration of EV signals. In addition to the fluorescence, light scatter signals also contain valuable information that it is often lost within the background noise. By using an open-architecture flow cytometer, we optimized the configuration by combining differently sized hardware components to improve light scatter-based EV detection by reducing optical background signals. Lastly, we applied these technological advancements in the field of high-sensitivity FCM to investigate interactions between biological particles, namely EVs and lipoprotein particles (LPPs), which are known to co-isolate. Liquid biopsy EV-based biomarker profiling is complicated because LPPs in blood outnumber EVs by orders of magnitude and have biophysical overlapping properties with EVs, including size and density. We here demonstrate by high-sensitivity FCM, in combination with orthogonal methods; i.e., Rayleigh and Raman scattering and cryo-electron microscopy, that EV-LPP complexes can be formed and can impact EV-analysis in blood. Overall, the findings presented in my thesis can contribute to the development of robust liquid biopsy EV biomarker profiling by FCM and shed light on the complexity of single EV analysis in body fluids, which will ultimately help to a successful clinical translation of EVs as disease biomarkers.

Creative problem solving (CPS) abilities are vital in today’s rapidly changing society. Teachers play a crucial role in promoting CPS from as early as primary education. This PhD project focused on CPS in primary school students. A CPS assessment procedure was developed and four studies were conducted to gain insight into the nature of CPS in this age group, as well as to formulate implications for the assessment of CPS. The studies examined the impact of exploring knowledge and defining problems on CPS outcomes, the feasibility of solution-finding, and how students select the most creative ideas. They also explored the generalization of CPS scores across tasks and the impact of raters and tasks on these CPS scores. In addition, the studies investigated the nature of CPS in primary school students by focusing on the CPS process and product. Lastly, it was examined whether elementary school teachers could assess students’ CPS abilities in absolute and relative ways. The findings showed that exploring knowledge and defining problems could assist primary school students in generating more and more original ideas. Furthermore, students were able to identify their most creative ideas. The studies also indicated that primary school students possess CPS abilities, but a comprehensive understanding of a student’s CPS abilities requires multiple tasks and raters. Relative assessments of CPS may yield reliable scores, whereas absolute assessments are more difficult to generalize across tasks. Teachers were quite able to assess CPS, in a relative way. The studies' results have not only provided relevant implications for education but also have opened up novel areas of investigation

This research was motivated by the impact of the diffuse P sources coming from agriculture on surface water quality and eutrophication. The goal of this Ph.D. thesis was to contribute to improving the management of drained agricultural areas to optimize the retention of Fe-associated P. Because of the legacy P in the soil, P-retention measures are needed in agricultural catchments to improve the surface water quality in the short- to middle-term. The first part of this thesis focused on monitoring the P transport at the farm scale. The second part of this thesis focused on investigating the mechanisms of P-retention by iron-coated sand (ICS), a Fe-rich by-product from drinking water production.

This dissertation develops a theoretical vocabulary to interpret and discuss ‘sociocultural interventions.’ This term refers to acts of public, political expression other than deliberative argument, such as rallies, public fasts, street art and vigils, which performatively alter elements of the social imaginary that structures political life. Although sociocultural interventions can profoundly affect the relationships between differently positioned political actors as well as the outcomes of collective decision-making processes, they have not typically received the political philosophical attention that they deserve. This is due in part to the great influence of theories of the public sphere that almost exclusively focus on the public justification of political positions by means of deliberative argument. Taking sociocultural interventions into account helps us to approach the public sphere not exclusively as a site where citizens seek to convince people subscribing to other points of view, but also as a site where political actors engage with historical traditions of existential meaning-making in creative, innovative ways. This is also important from the vantage point of a commitment to principles of democratic equality. Sociocultural interventions can serve to challenge widely held perceptions of which social groups are recognized as equal members of society and as legitimate participants in public debate. Moreover, they can also change the way in which resonant cultural elements that play an important role in political discourse are widely understood. Finally, sociocultural interventions can enable political actors belonging to marginalized groups within society to strengthen the resolve, sense of purpose and collective identity within these groups. Drawing upon Hannah Arendt’s reflections on action, authority, and augmentation, the dissertation argues that political actors stand in a dual relation to the perceived authority of the cultural elements that their sociocultural interventions draw upon: on the one hand, the authority of cultural elements is what inspires their contributions; at the same time, these contributions themselves forward novel interpretations of these same cultural elements. This dual relation makes it possible for authoritative cultural elements to simultaneously provide a sense of transhistorical continuity as well as a sense of empowering agency to political actors. For political actors, the engagement in collaborative meaning-making through sociocultural interventions drawing upon historical traditions of existential-meaning-making can be an inherently satisfying endeavour. Recognizing this enables us to better understand what motivates people to engage in political action, in spite of sometimes high risks of frustration, disappointment, and political repercussions.

Mudbanks are pertinent features that can be found along the 1,500-km long coastline of the Guianas. These mostly subtidal banks migrate from the mouth of the Amazon River in Brazil, along the coasts of French Guiana, Suriname and Guyana to the Orinoco Delta in Venezuela. When present the protect the coasts against incoming waves and simultaneously provide a source of sediment for coastal expansion and consequential mangrove colonization. Due to the alternating phases of erosion and expansion, that can last up to 10 – 15 years, is remains difficult to implement long-term coastal management strategies in the Guianas. This is especially true for coastal protection, nature conservation, CO2 storage and the development of agriculture and aquaculture. The limited accessibility of the coastal zone and the challenge to demarcate mudbanks in satellite observations make it difficult to differentiate between natural changes and those caused by mudbank migration, human interference or the impact of climate (change). Satellite observations offer the potential to improve future predictions, accurately analyse indicators of changes in mangrove forests and timely detect coastline changes on local- to regional scales. In this thesis a remote-sensing workflow was developed to utilize the spatial and temporal resolution of the complete Landsat satellite data archive since 1986 for the detection of mudbanks and associated coastal changes. This information was used to quantify the effect of mudbanks on local scale for the purpose of coastal management. We show that on average 32 m/yr coastal expansion occurs when mudbanks are present and 4 m/yr of erosion when absent. Large spatial- and temporal variations however highlights the importance variable mudbank footprints, caused by currents and waves, for the duration of coastal protection and natural changes that occur in mangrove forests.

Rethinking the Republic of Letters offers a revisionist look at the historiography of the Republic of Letters and the European, early modern community of learning. It suggests a new approach by conceptualising the learned world as an imagined community, since the members did not know all their peers. These learned communities formed through distinct memory cultures and the representation and identification with collective identities. Rethinking the Republic of Letters looks at early modern biographical dictionaries (vitae), eulogies, letters, travelogues, and funerary monuments of early modern learned man to trace the (re)formation of these communities. It thereby offers a novel perspective on early modern learned communities: the Republics of Letters.

International audience; Grazing is recognized as a major process driving the composition of plant communities in grasslands, mostly due to the heterogeneous removal of plant species and soil compaction that results in a mosaic of small patches called micro-patterns. To date, no study has investigated the differences in composition and functioning among these micro-patterns in grasslands in relation to grazing and soil environmental variables at the micro-local scale. In this study, we ask (1) To what extent are micro-patterns different from each other in terms of species composition, species richness, vegetation volume, evenness, and functioning? and (2) based on multigroup structural equation modeling, are those differences directly or indirectly driven by grazing and soil characteristics? We focused on three micro-patterns of the Mediterranean dry grassland of the Crau area, a protected area traditionally grazed in the South-East of France. From 70 plant community relevés carried out in three micro-patterns located in four sites with different soil and grazing characteristics, we performed univariate, multivariate analyses and applied structural equation modeling for the first time to this type of data. Our results show evidence of clear differences among micro-pattern patches in terms of species composition, vegetation volume, species richness, evenness, and functioning at the micro-local scale. These differences are maintained not only by direct and indirect effects of grazing but also by several soil variables such as fine granulometry. Biological crusts appeared mostly driven by these soil variables, whereas reference and edge communities are mostly the result of different levels of grazing pressure revealing three distinct functioning specific to each micro-pattern, all of them coexisting at the micro-local scale in the studied Mediterranean dry grassland. This first overview of the multiple effects of grazing and soil characteristics on communities in micro-patterns is discussed within the scope of the conservation of dry grasslands plant diversity.

International audience; Mimicking and extending the gating properties of biological pores is of paramount interest for the fabrication of membranes that could be used in filtration or drug processing. Here, we build a selective and switchable nanopore for macromolecular cargo transport. Our approach exploits polymer graftings within artificial nanopores to control the translocation of biomolecules. To measure transport at the scale of individual biomolecules, we use fluorescence microscopy with a zero-mode waveguide set up. We show that grafting polymers that exhibit a lower critical solution temperature creates a toggle switch between an open and closed state of the nanopore depending on the temperature. We demonstrate tight control over the transport of DNA and viral capsids with a sharp transition (∼1 °C) and present a simple physical model that predicts key features of this transition. Our approach provides the potential for controllable and responsive nanopores in a range of applications.

The emergence and spread of infectious diseases, especially zoonoses, is complex and depends on various determinants or drivers, including their interrelations at the human-animal-environment interface (HAEI). This thesis provides novel insights into the potential role of environmental transmission in the epidemiology of selected exemplary zoonotic pathogens (i.e. Campylobacter, Shiga toxin-producing E. coli O157, and non-typhoid Salmonella) that are typically considered as foodborne pathogens. This was done mainly by studying some of their characteristics in the environment and the spatial associations between their incidence among human cases and the level of exposure to local livestock density, using novel methods within an eco-epidemiological framework. In addition, this thesis explored the potential human health effects linked to extreme rainfall events and the spatial association between the urban-rural gradient and the diversity and composition of the human gut microbiome. Chapter 2 showed that direct exposure to pluvial floodwater, and activities leading to such exposure, are associated with an increased risk of developing acute gastroenteritis (AGE) and acute respiratory infection (ARI). Chapter 3 showed that surface water is mainly contaminated with Campylobacter originating from meat-producing poultry (i.e. broilers and turkeys) in areas with high poultry densities and from wild birds in recreational waters and WWTP discharge points. The results of Chapter 4 and 5 revealed that human STEC O157 infections can be caused by environmental exposure to small ruminants besides food, while the route of transmission of Salmonella and Campylobacter to humans is mainly foodborne. Chapter 6 revealed that bacterial diversity, richness and composition of the adult gut microbiome are associated with the urban-rural gradient in the Netherlands. This thesis showed that we cannot ignore the role of the environment in mediating the transmission of (foodborne) zoonotic pathogens from livestock and wildlife, and more holistically in shaping the human gut microbiome. However, evidence about specific routes of transmission (e.g. air-borne, via soil or water) and their relative importance, was not provided. To study all interactions between different drivers of zoonotic pathogens, we recommend future studies to focus on pathogen transmission and their related transmission pathways to be able to look beyond the three drivers of change included in the One Health disease triangle (humans, animals, environment). This can be visualized by including a white triangle based on the Gestalt idea that the sum of visual perception is more than its parts. This triangle illustrates the interactions to include to study zoonotic pathogens and provide a complete picture of how they may influence one another. This visualization can be considered as a first step towards further unraveling the complexity of zoonotic pathogens by showing “the complete picture” or at least an attempt to do so, to, in the end, achieve optimal health outcomes.

The mass loss of Earth’s land ice has caused 114 mm of global sea-level rise between 1901-2018, or more than half of the total measured sea-level rise (202 mm) in that period. The Greenland ice sheet and its peripheral glaciers contribute 40 mm to this sea-level rise, and are expected to continue to lose mass during the next century. Future projections of the mass loss of grounded ice, including the Greenland ice sheet require robust projections of both surface, basal and calving processes. Climate models are often used to calculate the surface mass balance of the ice sheet, defined as the precipitation minus the ablation at the surface. By far most of the ablation originates from the runoff of meltwater in the low-lying ablation zone, which covers only 10-16% of the ice sheet’s surface. In the ablation zone, surface melt is mainly driven by the absorption of solar radiation and by the sensible heat flux (SHF). The latter is defined as the vertical turbulent exchange of sensible heat between the surface and the overlying atmosphere. This turbulent heat flux explains most of the surface melt variability, and can become the major source of energy during extreme melt events. However, very few direct observations of turbulent fluxes exist on the ice sheet, which makes the simulation of these fluxes in climate models uncertain. This thesis aims to better quantify and therewith reduce the uncertainty of modelling the SHF across the Greenland ice sheet. In chapter 3, we address the issue of measuring the SHF on the ice sheet. A vertical propeller eddy covariance (VPEC) method is developed for this purpose. The large attenuation of the turbulent flux due to the large response times of propeller anemometers is accurately modelled, since the response times and the spectral characteristics of the turbulent fluxes are known. In chapter 4, we develop a novel method to map the surface aerodynamic roughness using either uncrewed aerial vehicles (UAV) or satellite laser altimetry (ICESat-2). A simplified model for obstacle height and drag is able to reproduce the in situ measurements. We develop a first map of surface aerodynamic roughness of the ice sheet which quantifies by how much the surface roughness decreases with elevation. In chapter 5, a model for the surface aerodynamic roughness is developed. A realistic description of the variation in the height of ice hummocks is found to yield a more accurate SHF, and therefore more accurate surface melt. In chapter 6, we build a database of surface energy balance fluxes from 25 automatic weather stations on the ice sheet, and we compare these measurements to the regional climate model RACMO2.3p2 with the updated roughness parameterizations from the previous chapter. We continue to monitor ice sheet surface processes with weather stations, eddy covariance and surface mass balance observations. Together with remote sensing observations, these can be used as benchmarks for the development of future models. The improvement of climate models allows for more accurate simulations of future ice sheet mass loss.

In primary school, teachers and students share responsibility for students’ learning progress. Teachers are expected to provide ‘adaptive’ or ‘differentiated’ instruction: making instructional decisions that are adapted to the diverse needs of individual students. Students are increasingly expected to (learn to) self-regulate their learning. Consequently, teachers are also increasingly expected to help students develop their self-regulated learning skills. The effectiveness of self-regulated learning and adaptive instruction hinges on the accuracy of two key processes: Monitoring and regulation. Monitoring refers to evaluations of individual students’ performance. Regulation refers to decisions about what subsequent activities (e.g., restudy, additional practice, or additional instruction) learners should engage in to improve their performance. Prior research showed that there is much room for improving the accuracy of students’ and teachers’ monitoring and regulation judgments. This dissertation presents studies on how this accuracy can be improved; zooming in on primary school students’ and teachers’ monitoring and regulation judgments of students’ performance on mathematical problem-solving tasks. The main findings support the idea that providing students and teachers with access to information that is highly predictive of students’ actual performance, improves their monitoring accuracy. That is, having students self-score their answers made their judgments of their own performance more accurate and providing teachers with access to students’ scores on similar prior tasks, made teachers’ judgments of their students’ performance more accurate. Yet, when it comes to developing students’ regulation skills, both students and teachers seem to need further support: Even after self-scoring, many students still need help from their teachers to make accurate regulation decisions, while teachers have difficulty identifying which students need their help in doing so. These findings set the stage for further research on how providing students and teachers with information that is highly predictive of students’ actual performance can improve the effectiveness of students’ self-regulated learning and teachers’ differentiation practices.

The first study in this thesis is designed to cover both approaches of Marchenko redatuming and Marchenko-based primary estimation in a range of subsampling scenarios, providing insights that can inform acquisition sampling choices as well as processing parameterization and quality control, e.g., to set up appropriate data filters and scaling to accommodate the effects of dipole fields, or to help ensuring that the data interpolation achieves the desired levels of reconstruction quality that minimize subsampling artifacts in Marchenko-derived fields and images. With the aim of handling short-period multiples in laterally-varying media with complex, realistic layering, a updated augmented Marchenko approach is proposed in this thesis providing reliable approximations to the problem. This is achieved through combining an adapted 1.5D approach and additional post-processing with the recently proposed Marchenko-type demultiple formula. The new approach and its limitations are demonstrated with a range of numerical models that capture varying degrees of lateral heterogeneity in the overburden. The benchmark of migrated images after removing short-period multiples suggests promising applications to field data. As a next step in the reflection-data processing chain, the migrated images are nonetheless blurred with uneven amplitudes and low resolution, despite a successful multiples removal procedure. As an important post-processing step, seismic image deblurring aims to enhance both the resolution and amplitude balance. Conventional methods developed for such an inverse problem are usually cumbersome and slowly converging, and require case-dependent user interference, e.g. in the form preconditioning, and the fine-tuning of free parameters for regularization. This thesis proposes instead to address the problem with a physics-based Machine Learning technique --- the (invertible) Recurrent Inference Machine, by explicitly employing the point spread function as the forward operator and thus prior information. With a simple almost-flat synthetic training model and very cheap computational costs, this new approach outperforms the benchmark of the invertible UNet - a commonly-used convolutional neural network architecture - in a series of tests, designed with both complex synthetic models and the field data application. Different from the other methods, the neural networks in this study are trained to map the migrated image into an impedance perturbation model, rather than the reflectivity model, making it closer to the next-level application of the impedance inversion for reservoir characterisation and time-lapse monitoring. In the last part of the thesis, a general and unified framework is proposed, which flexibly incorporates all kinds of interface conditions for any type of wave equations. With the new approach, the implementation of a wave equation solver can be benchmarked by comparing different angles of plane wave simulations to transmission/reflection coefficients from plane-wave analysis with exact boundary conditions. This is illustrated with the case of poroelastic wave equation, which has a wide application in rock physics and reservoir monitoring.

International audience; Terra Antiqua is a plugin for QGIS to make paleogeographic reconstructions with a user-friendly graphical interface. The goal of Terra Antiqua is to make paleogeographic reconstructions accessible and attractive to a much wider range of users, typically Earth and Life scientists and students, without extensive expertise in programming and GIS analyses. Yet Terra Antiqua can also be attractive for GIS developers as our reconstruction algorithms are accessible through application programming interfaces (APIs), open source Github repository and written in python with open standards (e.g. OpenLayers, OGC, GDAL). Starting from physiographic features and datasets rotated back to the desired reconstructed age (typically using Gplates), the previous release of Terra Antiqua offered a set of primary tools to run the main steps of a global reconstruction (1. Combine topo-/bathymetry, 2. Set Paleoshorelines, 3. Modify topo/bathymetry and 4. Create topo/bathymetry) and secondary tools to improve and enhance the result. From this first simple release we are incrementally adding tools and features inspired by various methods developed by experienced paleogeographers. The new release, Terra Antiqua 2.0, has integrated a new set of options on the existing tools, including the ability to create physically realistic geomorphic features. These new options will be presented within the controversial example of the reconstruction of the India-Collision and the development of the Tibetan-Himalayan orogen. Several reconstructions stemming from competing topographic and geodynamic models are thus compared and assessed based on compiled datasets including updated paleoaltimetry.

This dissertation provides insights into the systematic regional variations in news content and unveils their regional determinants as well as their influence on the behavior of economic agents. It features three chapters with empirical analyses of the German news media, each addressing one of three distinct relations in the complex regional news system. The first chapter evaluates the potential of media data to capture regional peculiarities. The following chapter digs deeper into the alignment of regional news media coverage and innovation activities in regions. The third chapter shows how regional news content explains differences in people’s behavior. The dissertation establishes two news-related datasets at the sub-national level in Germany. In sum, the dissertation contributes to a better understanding of sub-national heterogeneity in media reporting, by showing that regional differences in media content both explain and are explained by regional socio-economic characteristics. The findings substantiate the potential of analyzing subnational developments through the use of news media information. Thereby this dissertation marks a starting point and reference for scholars aiming to study this promising area of research.

Conventional wisdom suggests that one photon that carries one unit of angular momentum can change the spin angular momentum of a magnetic system with one unit (delta Ms = +-1) at most. This would imply that a two-photon scattering process can manipulate the spin angular momentum of the magnetic system with a maximum of two units. Here we examine the fundamental limit of the photon-driven transport of angular momentum by studying the magnon spectrum of {\alpha}-Fe2O3 using resonant inelastic x-ray scattering. We discovered a cascade of higher-rank magnons carrying double, triple, quadruple, and quintuple the spin angular momentum of a single-magnon. Guided by theoretical calculations, we reveal how a two-photons scattering process can create exotic higher-rank magnons and the relevance of these quasiparticles for magnon-based applications., Comment: Work presented as an invited talk by Hebatalla Elnaggar at the IXS conference 2021 https://www.bnl.gov/rixsrexs2021/

Organisations increasingly offer worksite health promotion to improve the health and lifestyle of their employees, and also because doing so has been reported to affect employees’ productivity. Worksite health promotion can take diverse forms, such as offering healthy food choices in the worksite cafeteria and providing onsite fitness facilities. Many of the positive effects of worksite health promotion are contingent on the extent to which employees use it. However, only a limited number of employees does so, and there is large variation between organisations in employee participation rates. In this dissertation, Anne van der Put studies the role of the work environment in explaining worksite health promotion use and its outcomes. To do so, she makes use of unique multilevel survey data of employees in many organisations from nine European countries. In addition, she designed and conducted a survey experiment to examine worksite health promotion for employees working from home. This dissertation contributes by answering novel questions in relation to worksite health promotion and health behaviours in the context of work. By focussing on the work environment, and social relations at work in particular, it contributes to understanding how to increase worksite health promotion use. A unique dataset with ample variation in work environments allowed for this. Furthermore, the research shows that worksite health promotion exceeds the physical limitations of the workplace, by accounting for the new context of hybrid work. Finally, examining different types of worksite health promotion provides deeper insights into its use and outcomes. The results show that the work environment matters for the use of worksite health promotion, both in the workplace and when employees work from home. Especially colleagues are important for the healthy choices employees make, both at work and outside. When colleagues encourage a healthy lifestyle, employees are more likely to participate in worksite health promotion, eat more fruits and vegetables and engage in more physical activity. The more colleagues participate in worksite health promotion, the more employees do so too. Additionally, the healthy eating behaviours of colleagues are related to each other. Organisational culture also plays a role. When work goes above and beyond everything, employees refrain from using worksite health promotion. These findings also apply when employees work from home: employees are more likely to use worksite health promotion when their colleagues do so and when this takes place during working hours. By allowing this, the organisation signals that they care about its employees’ well-being. This research furthermore shows that higher educated employees are more likely to use worksite health promotion, which translates in increased health inequalities. Employers also benefit, as the results indicate that employees that use worksite health promotion, but also those that are aware of its existence, perform better. Employers should ensure all employees are provided access to worksite health promotion, are aware of its existence and are able to use it. This will help employees in being healthy at work.

Photonic crystals are prime candidates for photonic integrated circuit applications, for instance in the field of optical telecommunications and optical computing. In this thesis we experimentally investigate the optical nonlinear behavior of single and coupled nanoresonators on a waveguide in a photonic crystal membrane. Photonic crystal resonators typically have small mode volume and high quality factor, which optimizes the optical nonlinearity crucial for active photonic devices. Our photonic crystal is designed for near-infrared light (~1550nm). Thermo-optical nonlinearity is the dominating nonlinearity in these InGaP photonic crystal waveguides. We measure the thermo-optical timescale in the system and find that the decay time of a mode is related to its mode profile. Additionally, we present a nonlinear mode mapping technique to measure the mode profile. We use an out-of-plane blue pump beam to spatially scan the surface of the photonic crystal and probe the mode. The pump light is absorbed and serves as a local heat source. Nonlinear effects, caused by free carrier absorption of infrared input light, significantly improve the resolution with respect to linear mode map techniques. This provides a non-invasive far-field imaging technique. Lastly, we look at two coupled nanocavities that give rise to optical multistability in the nonlinear regime. We investigate the phase space of the system by exciting the system with a puls of the out-of-plane pump beam. It turns out the pump pulse triggers a state change that remains stable after the pump is turned off. In this way we, find different energy branches of the multistable system, where the position of the pump pulse determines the thermo-optical state the system stabilizes in. Using this effect we demonstrate a programmable multistable all-optical switch, where we walk through the multistable phase space of the system, addressing several thermo-optical stable states in the system by lowering or raising the internal energy of the hybridized cavities.

Floodplains are important ecosystems that play a vital role in storing water during times of high river discharge. Unfortunately, many floodplains have become disconnected from their rivers. To properly manage rivers and their floodplains, we need to better understand the complex interactions between water, soil, plants, and animals. Studying rivers and floodplains that are minimally impacted by human intervention can provide valuable insights. In this thesis, we compare two relatively undisturbed rivers, the Narew in Poland and the Songkhram in Thailand, to explore similarities and differences between floodplains in temperate and tropical climates. We examine vegetation zoning and spatial patterns in water quality, composition, and nutrient availability, which are related to flood characteristics like duration, depth, and frequency. We found that both floodplains act as nutrient sinks, storing rather than releasing nutrients to the river. Along the river, tall vegetation made up of bamboo (in the Songkhram) or reeds, grasses, and sedges (in the Narew) filters water and supports high productivity. As vegetation becomes less productive further from the river, the influence of river water decreases. This zoning creates a high level of biodiversity that's closely tied to river and floodplain dynamics. While nitrogen is the main nutrient that regulates vegetation in both areas, nutrient supply from flood water and soil varies by location and vegetation type. The Narew's water discharge is relatively constant year-round, whereas the Songkhram has highly variable discharges, with high peaks during post-monsoon flooding and very low discharges during the dry season. To develop effective floodplain conservation, management, and restoration plans, we need to consider climate, river discharge patterns, nutrient loads, local conditions, vegetation zones, and how these factors interact. Empirical studies like this one, though labor-intensive, can provide a better understanding of the system as a whole and enable a targeted approach to conservation and restoration efforts in different climate zones.

Why are European food systems stuck in a pattern of producing large quantities of food while greatly contributing to today’s environmental and social problems? And what can farmers, businesses, and governments do to get these systems “unstuck” and onto a more sustainable path? These questions lie at the heart of this thesis, which analyzes the potential transformation of the Dutch agricultural sector. The Netherlands is emblematic of Northwestern Europe’s farming sectors: the country produces large volumes of agricultural commodities, but per hectare of farmland it has the highest environmental pressure in the EU. By analyzing the context in which farmers work and transform their operations towards sustainability, we identify knowledge-related, sociocultural, economic, and regulatory types of lock-in that prevent a system-wide transformation. To overcome these types of lock-in, society must turn away from the persistent underlying paradigm of growth, resource extraction, and control over nature. The seeds of such a reorientation can be seen in the bottom-up transformations of individual farmers and regional initiatives. At the farm level, personal characteristics like risk appetite, open-mindedness, reflective ability, and sociability with peers are instrumental in overcoming structural barriers. At the regional level, we observed some signs of bottom-up reorientation through the facilitated redesign of a farm in collaboration with the farm’s stakeholders. On the national level, a clear and unambiguous vision, structural policies to level the playing field for farmers, and transition funds are needed to support the transformation.

The taxonomy of viruses is developed and overseen by the International Committee on Taxonomy of Viruses (ICTV), which scrutinizes, approves and ratifies taxonomic proposals, and maintains a list of virus taxa with approved names (https://ictv.global). The ICTV has approximately 180 members who vote by simple majority. Taxon-specific Study Groups established by the ICTV have a combined membership of over 600 scientists from the wider virology community; they provide comprehensive expertise across the range of known viruses and are major contributors to the creation and evaluation of taxonomic proposals. Proposals can be submitted by anyone and will be considered by the ICTV irrespective of Study Group support. Thus, virus taxonomy is developed from within the virology community and realized by a democratic decision-making process. The ICTV upholds the distinction between a virus or replicating genetic element as a physical entity and the taxon category to which it is assigned. This is reflected by the nomenclature of the virus species taxon, which is now mandated by the ICTV to be in a binomial format (genus + species epithet) and is typographically distinct from the names of viruses. Classification of viruses below the rank of species (such as, genotypes or strains) is not within the remit of the ICTV. This article, authored by the ICTV Executive Committee, explains the principles of virus taxonomy and the organization, function, processes and resources of the ICTV, with the aim of encouraging greater understanding and interaction among the wider virology community.

Children with Developmental Language Disorder (DLD) have persistent language difficulties in the absence of a clear cause. Their language difficulties can for example not be explained by hearing loss or a lack of language input. Many children with DLD also have executive functioning (EF) deficits. It is currently unclear how these EF deficits relate to their language difficulties. Children with 22q11.2 deletion syndrome (22q11DS) also frequently have speech-language problems. However, in their case the cause of these problems is known: a genetic disorder stemming from a missing piece of DNA on chromosome 22. This leads to a wide variety of symptoms, most prominently congenital heart defects, palatal abnormalities, and cognitive deficits. The multifactorial nature of DLD hampers our ability to shed light on the relationship between language impairment and EF deficits. The language impairment of children with 22q11DS all stem from the same underlying etiology (i.e., cause of a disease or condition), which might help reduce the complexity of this investigation. This dissertation explores whether 22q11DS can be used as an etiologically homogeneous model for studying the relationship between language and EF in children with DLD. We conclude that, although the groups are phenotypically similar regarding language profile and EF deficits, there are several limitations with using 22q11DS as a model for DLD. Nonetheless, a comparison between these groups is clinically relevant and provides interesting opportunities for fundamental research.

The adaptive immune system employs a large number of T cells, that each have a specificity defined by their T-cell receptor (TCR). Characterizing the TCR repertoire in humans and mice thus provides a way to address outstanding immunological questions. The repertoires can be assessed quantitatively using high-throughput sequencing. Interpretation of the resulting data is challenging due to the vast diversity of the TCR repertoire and the relatively small number of cells that can be analyzed in an experiment. In this thesis we analyze TCR repertoires from humans and mice, by integrating information obtained from multiple samples. In Chapter 2 we devise various models for the distribution of TCR clone sizes in the human naive T cell pool. We characterise the repertoire of TCR α and β chains by repertoire sequencing of various cell subsets. Comparing the model predictions with the experimental outcomes, we find solid evidence for the presence of very large TCRαβ clones in the naive T cell repertoire. These large naive T-cell clones are only partly explained by their generation probability. Chapter 3 aims to identify sequence characteristics of such abundant naive T cell clones. We find that the D segment is missing in a substantial fraction of the abundant TCR β sequences in the naive T cell repertoire of young individuals. Such sequences appear to be mostly generated before birth, to persist over a human lifetime, and, as a result, to be excessively shared between individuals. Chapter 4 is a short unpublished report on quantifying the effects of age on the TCR repertoire. We present example analyses and discuss potential pitfalls of assessing ageing effects on the TCR repertoire. Chapter 5 describes a pilot study on using TCR sequencing to follow the T-cell response upon pneumococcal conjugate vaccination. We define quantitative requirements to classify T-cell expansions but detect these in only a minority of the donors. Our analysis suggests that the vaccine-induced T-cell response is small and/or very broad, and highlights experimental requirements to characterise such responses in future studies. A key limitation of the studies in humans introduced above is the limited number of cells that can be analysed compared to the total size of the T-cell pool. As a result, many of the TCR chains that make up the diversity of the TCR repertoire will be missed in any analysis. Bulk sequencing methods also do not uncover the full TCR diversity present in an individual as the information on the pairing of α and β chains is not obtained. In Chapter 6 we overcome both limitations by studying the TCR repertoire of one-TCRa mice that have a strongly reduced receptor diversity. This allows us to study the nearly complete TCR diversity, and compare the repertoire among individual lymph nodes and mice, revealing a spatial organisation of the TCR repertoire. In summary, this thesis describes how we studied T-cell biology quantitatively by combining simplified models with careful bioinformatics analyses.

This thesis focused on the fabrication of kidney proximal tubule models. A coaxial printing system allowed the robust and straightforward fabrication of coiled perfusable microfibers, replicating the kidney proximal convoluted tubules. The formed alginate-gelatin microfibers offer a complex tubular shape in which the cells exhibit mature markers, such as functional transporters and polarized monolayers. Moreover, this model has proven to support both healthy and cystinotic proximal tubule cell lines and allows for mechanistically studying tubulopathies. Though distinctive 3D-printing strategies offer opportunities to mimic the structure of the convoluted proximal tubule, printing resolution remains restricted for soft hydrogel materials. Therefore, a unique method was studied, whereby post-printing treatment induced resolution enhancement of methacrylated hyaluronic acid / gelatin methacryloyl (HAMA/GelMA) based hydrogels. This shrinking technique opens a wide application to create higher resolutions for 3D printing. However, we also found that the polycationic shrinking reagents used were relatively toxic for our cells of interest. Therefore, more research is needed to prevent the direct exposure of cells to the shrinking reagents. Using melt electrowriting, scaffolds were fabricated that are self-supportive, yet small-sized and highly porous. These scaffolds enable direct access to the basolateral and luminal cell sides to facilitate solute exchange with vasculature in immediate proximity, which is critical for functional proximal tubule constructs. The polymer applied, polycaprolactone, is used to prepare biomaterials that generally show good biocompatibility and is compatible with implantation in the future. Kidney proximal tubule cells and glomerular endothelial cells form polarized monolayers in our tubules, form their own extracellular matrix, and show functionality for important kidney transporters. In our preliminary results we showed initial work with induced pluripotent stem cell-derived kidney organoids, which is very promising for the development of implantable proximal tubule grafts. The aim was to develop vascularized kidney tubules for studying the clearance of protein bound uremic toxins, while gaining knowledge for the development of implantable kidney tubules. The developed models allow for studying transepithelial secretion of uremic toxins, an overarching application for these constructs. This will further advance our understanding of the kidney secretion processes and aid in studying interventions to main proximal tubule function in chronic kidney disease conditions in vitro. Furthermore, while fundamental by nature, our research deliverables enhance mechanistic insight in additive manufacturing and kidney development processes. Altogether, our findings will further advance the field of kidney engineering, while working towards kidney replacement therapies.

In early life, the intestinal microbiota undergoes a tremendous maturation and if disturbed this has been associated with negative effects on immune and brain development. Healthy microbiome maturation is supported by components like prebiotics like short-chain galacto-oligosaccharides (scGOS) and long-chain fructo-oligosaccharides (lcFOS) and omega-3 polyunsaturated fatty acids (n-3 PUFAs), which are present in infant formula milk to mimic human breast milk. The aim of this thesis was to evaluate the additional effects of a dietary combination of scGOS:lcFOS and n-3 PUFAs to improve health as well as to prevent cow’s milk allergy (CMA) development. In healthy mice, the combination of scGOS:lcFOS and n-3 PUFAs altered the intestinal bacterial composition in a distinct manner compared to the effects of the compounds individually. Additionally, scGOS:lcFOS improved social behaviour and reduced anxiety-like and repetitive behaviour, however, the n-3 PUFA and the combined interventions had limited to no effect on the evaluated behaviours and immune system parameters. CMA development was partly prevented by scGOS:lcFOS or n-3 PUFAs in mice but no additional effect was observed by the combination. Splenic cytokine responses were reduced and caecal butyrate level was induced by scGOS:lcFOS which tended to be impaired when combined with n-3 PUFAs, indicating the individual effects seemed to be negatively impacted when combined. It could also suggest a possible interaction between the dietary components, which was evaluated by studying modulation of intestinal Pparɣ expression, physical and chemical interaction and modulation of intestinal bacterial fermentation. It is no option to leave scGOS:lcFOS or n-3 PUFAs out of formula milk but it is essential to evaluate individual components and combinations as they might act differently than expected.

Some conflicts between people are intractable. This means that they are difficult or impossible to solve, and carry the risk of serious escalation. There are situations in which this intractability can only be understood well in relation to needs and/or values that those involved consider essential to who they. I refer to these as “identity-related moral conflicts” (IRMC). A person’s identity is not subject to negotiation. Therefore, the question arises: How can a stable and just solution be conceived for such conflicts? Several influential philosophical theories, such as Rawls's Political Liberalism, Habermas's Discourse Ethics and Taylor's Articulation Ethics, fail to discern a stable and just solution to such conflicts. The solutions these theories foresee are possible only when conditions are met - conditions that are not met in an IRMC. My proposal to distinguish a just and stable solution is by combining the strengths of Discourse Ethics and the Articulation Ethics so that they can compensate for each other's weakness. This leads to a theory of the Transformative Dialogue with which stable and just solutions of IRMCs can be worked out. The core of this theory is that authenticity should be understood not individually but intersubjectively. In other words, what is authentic to someone is determined in simultaneous, mutual relationships of recognition that are constituted by a Transformative Dialogue. The Transformative Dialogue refers to the process in which parties in an IRMC transform their self-understanding and their understanding of the other in such a way that a relationship of mutual recognition emerges. The intractable conflict is thereby resolved in a stable and just manner. To be able to solve intractable conflicts in practice, it is necessary to make the Transformative Dialogue part of institutions so that a transformative-deliberative society arises in which the transformation of self-understanding is as common as the exchange of factual arguments or negotiation.

Contract law offers protection to those who are defined within the legal system as weaker parties, such as employees, tenants, and consumers. In that context, weaker parties are routinely and tacitly assumed to be acting in an economically rational manner, an assumption that affects when and how they are offered protection. But insights from behavioral science reveal that people – and thus weaker parties too – have limited cognitive capacities and by no means always make economically rational decisions. The discrepancy between how weaker parties actually make decisions and how they are assumed to do so leads to deficiencies in their current protection. Although nudging could potentially be applied to better protect weaker parties without restricting their freedom of contract, no systematic research yet exists on how to do that and what the limits are to how that may be done. This doctoral thesis therefore answers the question as to how nudging can contribute to the protection of weaker parties in Dutch contract law. The first part – the legal framework – addresses why, when and how weaker parties are protected, and the deficiencies of their current protection. The second part – the theoretical framework – analyses the potential and limits of using nudging to protect weaker parties. The third part – the case studies – applies the theoretical framework to three issues concerning the protection of weaker parties, illustrating the operation of the theoretical framework and making specific recommendations for applying nudging to protect weaker parties. Nudges that help people make their own decision can contribute to more and better protection in case various specific types of lack of cognitive capacity or lack of ability to resist occur and, indirectly, sometimes also to more and better protection in case a lack of information occurs. Steering nudges can contribute to more and better protection in case of a lack of cognitive capacity, a lack of ability to resist, or a lack of information in general. That nudging can help to protect weaker parties does not mean that it is a cure-all. Indeed, the scope of nudging is limited by several factors. But this is no reason to be skeptical about nudging to protect weaker parties in general. That would amount to throwing the baby out with the bathwater. After all, the research presented here provides a theoretical framework on the basis of which legal practitioners, including legislative drafters and judges, can assess whether and, if so, how nudging can be applied to protect a weaker party. In doing so, this research contributes to a contract law in which adequate protection is offered not only to weaker parties who are economically rational, but also to real-world weaker parties.

Millions of microorganisms inhabit the soil, and some of them contribute to the growth of plants. To gain a better understanding of how microbes promote plant growth, we compared the impact of fungal and bacterial communities on litter decomposition and plant growth. Our study revealed that fungi are the primary decomposers of plant litter and have a more significant impact on nutrient cycling than bacteria. Furthermore, when the fungal and bacterial communities are working together, they have a complementary effect on plant growth that is greater than when either community is working alone. Arbuscular mycorrhizal fungi (AMF) are recognized as one of the most important types of plant symbiotic fungi. AMF are widely distributed in the soil and colonize plant roots. They can functionally extend the plant root system and reach areas in the soil beyond the plant roots, enabling plants to access more water and nutrients. We investigated the microbial community that surrounds the AMF hyphae and discovered that fungal hyphae are colonized by specific bacteria and protists. We demonstrate that the microbes that colonize the fungal hyphae play a crucial role in facilitating mycorrhizal development and assisting plant nutrient uptake. We showed that a particular bacterium of the genus Devosia is associated with AMF hyphae, interacts synergistically with the mycorrhiza and promotes plant growth by facilitating nitrogen uptake. This thesis deepens our understanding of the interactions between different microbial groups and their roles in litter decomposition and mycorrhization processes. A better understanding of plant-microbe interactions is essential for the development of effective application strategies for plant-growth-promoting microbes in agriculture. Ultimately microbes can help create sustainable agricultural practices that rely less on chemical inputs for food production.

Glycans, also known as carbohydrates, are an essential component of all living organisms and are, among other things, present on proteins and lipids in the cellular membrane. Glycan structure is not encoded in the genome, oppose to other biopolymers such as RNA and proteins, but is regulated by around 200 glycosyltransferase enzymes. As a result of substrate competition, glycans are generated that vary in composition, linkage positions and configurations. The large heterogeineity and diversity in glycan structure complicates the synthesis and characterization of glycans. The research described in this dissertation covers the synthesis of well-defined glycans and applies synthetic glycans in the development of analytical methodology for the detailed structural characterization of naturally occuring glycans. Chemically modified glycans are synthesized by employing glycosyltransferase enzymes. These modifications are manipulated to (de)activate certain positions on a glycan and thereby direct glycosyltransferase activity. Traceless removal of these modifications allows the synthesis of complex natural glycans with controlled architecture. These glycans are used to study differences in biological activity between isomeric glycans. Synthetic glycopeptides with various functionalities are used as model substrates to develop a method to release glycans from glycopeptides without damaging the glycan structure itself. This methodology conserves labile glycan modifications that are often lost in glycan analysis. Furthermore, synthetic complex glycans are applied as reference compounds for the development of analytical methodology that can structurally elucidate challenging glycan epitopes.

Glaciers exert a strong influence on the hydrography and biogeochemistry of arctic fjord environments through the export of large quantities of particles, freshwater and ice. In turn, they contribute to the shaping of the marine ecosystems upon which local communities are heavily reliant. Extensive glacial changes are occurring along the coasts of Greenland, and their effects on the surrounding fjord environments are becoming increasingly visible. Motivated by the question of what we can expect in terms of future fjord transformations under continued glacial retreat, this thesis examines seasonal patterns in two neighbouring fjords on the southwest coast of Greenland: Nuup Kangerlua (Godthåbsfjord), a predominately marine-terminating system, and Ameralik, which receives glacial meltwater only from a land-terminating glacier. Seasonal differences in hydrography, biogeochemistry and ecology are described, with an emphasis on the role of glaciers in these processes.

Plastic waste is causing a high amount of pollution in the ocean. A lot of plastic pollution remains afloat, and accumulates in the subtropical gyres, in the so called `floating garbage patches’. It is not well known where most of the marine plastic pollution actually comes from: is it for example originating from rivers, coastlines, or from fishing activity? Furthermore, it is relatively unknown which processes `remove’ plastic pollution from the surface of the ocean over time. A part of the plastic pollution ends up on the coastlines, starts to sink down after some time (e.g. under the influence of biofouling), or fragments into smaller particles. Current estimates of the amount of plastic pollution that is floating in the ocean are much lower than the estimated amount of plastic pollution entering the ocean every year. This thesis is written within the TOPIOS project (Tracking Of Plastics In Our Seas), where we attempt to resolve this discrepancy. In order to estimate the marine plastic mass budget, we combine numerical simulations of the ocean with observational measurements of plastic concentrations. One way to do this is by using statistical models. We use a regression method (random forests) to estimate the total amount of litter on the Dutch North Sea coastline. Using a beach cleanup dataset from The North Sea Foundation, we train a model to predict the amount of beach litter as a function of various environmental variables (e.g. conditions related to the weather, ocean currents, tides). This model is used to extrapolate the amount of litter along the entire Dutch North Sea coastline (about 16,500-31,200 kilograms in total). Observational data of plastic concentrations can also be used to calibrate unknown parameters in numerical models of the ocean currents. We do one such analysis for the Mediterranean Sea, and a more extended analysis for plastic pollution on a global scale. One important conclusion is that the total amount of plastic pollution in the global ocean is likely much higher than previously expected: roughly 3,000,000 tonnes (3 billion kilograms). Previous research, with estimated quantities of roughly 250,000 tonnes, mainly used net measurements to calibrate the numerical models. These kind of net measurements are likely to miss larger objects, which contribute to most of the marine plastic mass. In our analysis we additionally use data of larger plastic objects from visual observations (both from the open ocean and from beaches), and net measurements from the water column for the model calibration. We estimate that a relatively large part of marine plastics sinks down over time (for example due to growth of algae). Fragmentation of plastics plays an important role: most of the plastic items found in the ocean are small fragments originating from larger objects. We estimate that beaches contain about 2% of the global marine plastic mass. We estimate that the input of plastic pollution is much lower than previously expected, and that residence times of plastic items in the marine environment are higher than previously anticipated.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed at early age and characterized by impairments in social behavior and communication, the presence of repetitive behavior, and deficits in learning and memory. Two of these bacterial metabolites, p-cresol sulfate (pCS) and 4-ethylphenyl sulfate (4EPS), are reported to be enhanced in feces, blood and urine of people diagnosed with ASD. Moreover, pCS and 4EPS exposure in mice induces ASD-like behavior, but the exact underlying mechanisms remain to be investigated. Many sheddases are members of the so-called ADAM family and potentially play a role in the pathogenesis of disease. In addition, the phosphatase PTEN is an important enzyme that regulates neural cell growth and survival in the brain. This thesis is focused on gaining insights into the possible molecular mechanisms of bacterial metabolites in the pathogenesis of ASD to identify potential new targets for intervention and therapy. Using an ASD mouse model and a neuroinflammation cell model, this thesis illustrates that pCS and 4EPS affect the level and function of the sheddases, ADAM10 & ADAM17, and the phosphatase, PTEN, that might be involved in the ASD-associated changes in neuroimmune responses and/or neuronal network function in the brain. In addition, this thesis further identifies that ADAM10 might be a treatment-target for ASD either through direct pharmacological inhibition or via nutritional interventions targeting bacteria that are involved in pCS and 4EPS production aimed to reduce ASD-associated detrimental symptoms.

This history of deep brain stimulation in psychiatric disorders describes how the idea, technology and scientific and clinical application of deep brain stimulation developed, from the first research with brain electrodes around 1860 to modern therapeutic deep brain stimulation. From 1860, brain researchers knew how to relate specific cortical brain areas to certain (sensory and motor) functions with increasing precision through ablation and electrical stimulation, and neurosurgeons explored the first diagnostic possibilities of cortical electrical stimulation. Between the 1920s and 1950s, backed by unprecedented philanthropic investment, particularly from the Rockefeller Foundation, a scientific psychiatric approach developed in which biological, psychological and social aspects were considered important. From this holistic perspective combined with psychoanalytic insights, doctors and researchers in the 1930s and 1940s tried to understand and improve two important new somatic treatments of the 1930s - electroconvulsive therapy and psychosurgery. From 1948, the first experiments with deep brain stimulation in psychiatric patients took place in the United States. Deep brain stimulation managed to develop into its own experimental field with the support of the American government, but the clinical experiments did not yield unambiguous results. The psychoanalytic interpretation of patients' experiences during stimulation also proved to be difficult. Despite these problems, all stakeholders and institutions believed that deep brain stimulation was a promising therapy. In the 1950s during the Cold War, deep brain stimulation in the United States contributed to the fear of (massive) mind control and behavioural manipulation of civilians. Doctors and researchers had found several brain regions in the midbrain in particular that they could use to influence behaviour of their laboratory animals through deep brain stimulation. In the 1970s, the anti-psychiatric movement strongly protested against electroconvulsive therapy and (plans for) experiments to influence unwanted and/or aggressive behaviour of psychiatric patients and prisoners through psychosurgery and deep brain stimulation. Doctors were, on the contrary, working hard at the time to optimise their psychosurgical procedures, both technologically through the stereotact and neuroanatomically through standardisation of some brain regions relevant to psychopathology. Discussions between proponents and opponents were conducted from strong utopian and dystopian perspectives with overly high expectations of the possibilities of deep brain stimulation. Investigations of the American government in the 1970s revealed a serious lack of national oversight of the above developments. This contributed to new medical-ethical guidelines on informed consent, among other things. However, controversy meant that by the end of the 1970s, psychosurgical procedures, including deep brain stimulation, hardly ever took place, despite new scientific understanding that they could be effective treatments for certain psychiatric patients and could not be used for mind control. During the reintroduction of deep brain stimulation for psychiatric disorders in the late 1990s expectations were again high. This was despite the fact that the treatment had remained virtually unchanged and it was not - and still is not - well understood how it works. The positive results of small-scale experiments were not replicated in better-controlled large-scale effect studies. Unlike in the 1970s, there was relatively little critical discussion.

Transport of charge, fluid, and salt in electrolytes is critical for biology, where it nurtures cells, and also for industry, where it is used to purify our drinking water. Not only is transport in electrolytes important, but it also exhibits a rich variety of transport phenomena due to the intricate connection between ionic and fluidic transport. Striking examples are electro-osmotic flow, where a voltage difference drives flow, and streaming current, where a pressure difference drives charge transfer. In straight, micrometer, channels this transport usually exhibits a linear relation between driving force and transport rate. However, in this thesis we investigate transport in reactive and conical channels for which surprisingly transport is nonlinear. We show that flow alters the surface chemistry of a dissolving channel and that electrostatic surface-ion interactions induce nonlinear reaction kinetics. Finally we consider the influence of pressure and geometry on current rectification by conical pores. These nonlinear transport phenomena not only open up new signal-processing and chemical analysis methods, but also affect mineral transport by groundwater.

During a megathrust earthquake cycle, the plates accumulate strain in the interseismic stage due to locking of a portion of the megathrust that separates them. This strain is released during the earthquake and following rapid postseismic relaxation. As summarised in Chapter 1, this understanding was built through decades of seismological and geodetic observations and of advances in physics-based models. Models link properties and structures to observable quantities and are crucial to interpreting observed surface deformation in terms of the processes and materials in the inaccessible subsurface. However, different models can approximate the same observations roughly equally well. Furthermore, more sophisticated models do not necessarily reflect better the processes and properties of the subsurface; at the same time, simpler models that can explain some observations do not necessarily reflect the key processes occurring throughout one or multiple earthquake cycles. This thesis uses relatively simple three-dimensional models that still capture the key processes occurring during repeated earthquake cycles, without attempting to reproduce the structure or properties of specific subduction zones, to examine possible explanations of geodetic observations from global navigation satellite systems (GNSSs) at different stages of the cycle. Chapter 2 focuses on the interseismic, horizontal deformation of the overriding plate, which is in apparent contrast with observations of far-reaching coseismic displacement. We estimate the spatial patterns, with uncertainties, of GNSS velocities in South America, Southeast Asia, and northern Japan. Interseismic velocities with respect to the overriding plate generally decrease with distance from the trench with a steep gradient up to a “hurdle”, beyond which the gradient is distinctly lower and velocities are small. The hurdle is located 500-1000 km away from the trench for the trench-perpendicular velocity component, and either at the same distance or closer for the trench-parallel component. The trench-perpendicular hurdle generally follows major tectonic or geological boundaries and seismological contrasts. We formulate and test the hypothesis that both the interseismic hurdle and the coseismic response result from a mechanical contrast in the overriding plate. Our models show that overriding plates with a sufficient contrast respond to locked interseismic convergence similarly to observations. The compliance contrast is probably mainly responsible for the observed hurdle and in turn results from thermal, compositional and thickness contrasts. Chapter 3 is concerned with the increased landward velocities that were recorded onshore after 6 megathrust earthquakes in subduction zone regions adjacent to the ruptured portion. We investigate whether bending can be expected to reproduce this observed enhanced landward motion (ELM). We find that viscous relaxation, but not afterslip, produces ELM when a depth limit is imposed on afterslip. This ELM results primarily from in-plane elastic bending of the overriding plate due to trenchward viscous flow in the mantle wedge near the rupture. Modeled ELM is, however, incompatible with the observations, which are an order of magnitude greater and last longer. This conclusion does not significantly change when varying key model parameters. The observed ELM consequently appears to reflect faster slip deficit accumulation, implying a greater seismic hazard in lateral segments of the subduction zone. In Chapter 4, we study postseismic landward motion observed on the overriding plate in the vicinity of a major megathrust rupture. We argue that relocking of the megathrust, particularly at shallow depths, is needed for postseismic relaxation to produce landward motion on the tip of the overriding plate. We discuss how that this finding is consistent with previous simulations that implicitly relock the megathrust where afterslip is not included. We conclude that the Tohoku megathrust relocked within less than two months of the earthquake. This suggests that the shallow megathrust probably behaves as a true, unstably sliding asperity.

This thesis aims to advance the assessment of the future role of bioenergy as a climate mitigation option for the EU to mid-century. This is achieved by improving EU-level projections at a systematic level via accounting for the critical considerations within supply and demand dynamics across global and regional scales, traversing the full delivery-chain and attached emissions. To achieve this aim, the following research questions are addressed: 1. What is the projected role of bioenergy within EU decarbonisation strategies from quantitative assessment approaches, and how well do they capture 'Root-Chute' considerations? 2. To what extent could global bioenergy competition and trade constraints impact EU mitigation potential from bioenergy and vice-versa? 3. How consistent are integrated modelling assessments for bioenergy deployment with EU-level climate, energy and bioenergy-related policy targets? 4. How feasible are long-term projections for EU bioenergy deployment and mitigative potential from the perspective of logistics, scale-up, management practices and technological advancements?

Since the beginning of the COVID-19 pandemic in early 2020 until the time of finishing this dissertation in July 2022, the World Health Organization has registered almost 560 Million cases and well over 6 Million deaths from COVID-19. Although a number of effective vaccines have been developed and large-scale vaccination campaigns have been implemented globally, only 58 of 194 WHO member states have met the 70\% vaccine coverage goal. Thus, mathematical models of disease spread remain one of the most important tools to understand the spread of such dynamics and can thus support public health policymaking. Network models of disease spread, the type of model addressed in this dissertation, have the advantage of accounting for social structure. However, they typically neglect the changes in our social relationships, despite the evidence that people avoid public places or infectious others during times of increased risk of infection. This dissertation presents, to our knowledge, the first model for the co-evolution of social networks and infectious diseases considering theory from social sciences, health psychology and epidemiology. The Networking during Infectious Diseases Model (NIDM) describes networking decisions as a trade-off between the benefits, costs, and potential harms of infections that a social relation creates. Agent-based simulations suggest that disease avoidance may reduce infection numbers significantly, depending on risk perceptions and network structure. A large-scale networking experiment, however, suggests that when disease avoidance is prioritized over maintaining social relationships, the cost might be a loss of social cohesion.