Small RNA, big impact:Profiling the therapeutic potential of microRNA-132 in Alzheimer's disease

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with no effective disease-modifying treatments. The multifactorial nature of AD underscores the need for therapeutics that address multiple pathological pathways. MicroRNAs (miRNAs) are promising candidates due to their multitargeting nature and ability to regulate diverse molecular networks. Among them, miR-132 is notably downregulated in AD and has been linked to amyloid deposition, Tau pathology, synaptic plasticity, and memory decline. However, understanding its full regulatory effects, particularly in microglia and neurogenesis, is crucial for assessing its therapeutic potential.This thesis investigates the role of miR-132 in two key aspects of AD pathophysiology: neuroinflammation and adult hippocampal neurogenesis (AHN). First, a multi-omics approach was used to profile the miR-132 targetome in the mouse hippocampus, revealing its role in microglial activation and disease-associated microglial states. In human induced pluripotent stem cell (iPSC)-derived microglia, miR-132 modulation influenced gene expression and inflammatory responses, supporting its relevance in AD.Second, miR-132’s effect on AHN was examined by identifying its potential targets at the interphase between AD and AHN. NACC2 was identified as a key miR-132-regulated gene affecting neural stem cell differentiation, with potential relevance to AD.Overall, this thesis provides novel insights into miR-132’s regulatory roles in immune and neurogenic functions, highlighting its promise as a multi-target therapeutic strategy for AD. However, its pleiotropic effects necessitate careful evaluation to balance therapeutic benefits and potential risks

The fast and the direct:Unravelling gene regulatory networks with acute depletion strategies

DNA is the blueprint of life and every cell is specialized to use the DNA in its unique manner. This promotes cell identity and function. Around 2% of DNA contains genes, which encode for proteins. The activation and inactivation of genes are usually defined as gene regulation. Regulation of gene activity is generally promoted by the activity of transcription and the regulation of the molecular machinery that activates transcription. Most of the DNA does not contain genes, rather it contains DNA sequences with a regulatory function, defined as Regulatory Elements (RE). RE serve to activate genes at the appropriate level, time and space. Furthermore, the DNA is packaged into chromatin, a three-dimensional assembly of DNA and proteins that fits into the cell nucleus. Chromatin structure contributes to gene activation. Understanding the mechanisms of gene regulation is fundamental to deciphering cell behavior and function. In this thesis, we determine the dynamicity of various factors directly regulating gene activation. We used acute depletion strategies to study the direct role of key players involved in gene regulation, to determine the mechanisms of chromatin organization and to identify functional regulatory regions. Using a time-resolved determination of the early molecular events that control gene activity, we uncover novel insights into the factors involved in gene regulation, determining a novel function for chromatin dynamics, the main molecular factors important for gene regulation and develop a framework to identify RE relevant for gene activation

Efficient deep learning inference on end devices

Deep Learning (DL) has become a cornerstone of modern Artificial Intelligence (AI), powering applications across healthcare, computer vision, and autonomous systems. However, executing DL inference on resource-constrained end devices—such as smartphones and IoT hardware—poses challenges due to limited computational resources, energy constraints, and real-time requirements.This thesis addresses the optimization of DL inference on Heterogeneous Multi-Processing System-on-Chips (HMPSoCs), which integrate CPUs, GPUs, and Neural Processing Units (NPUs). It explores strategies that collaboratively utilize these processors to enhance inference efficiency in terms of latency, throughput, and power.A layer-wise switching strategy is proposed to assign each layer of a DL model to the processor that minimizes inference latency, improving responsiveness for time-sensitive applications like AR/VR. To enhance power efficiency, Dynamic Voltage and Frequency Scaling (DVFS) is combined with layer-switching, ensuring performance within battery constraints. Additionally, selective quantization is introduced to leverage NPUs without sacrificing model accuracy, assigning quantized and full-precision layers to appropriate processors. For throughput, a pipelined execution approach partitions models across CPU clusters, GPUs, and NPUs to process frames concurrently, meeting high FPS demands.As a key outcome, the ARM-CO-UP framework is developed to support profiling, processor switching, pipelining, and DVFS, enabling flexible and cooperative execution across processors.This work contributes toward enabling efficient DL deployment on everyday devices, balancing performance, energy, and accuracy. The proposed methods and framework provide a practical foundation for continued research in efficient AI computing at the edge

From trends to theories in urban mental health

This thesis addresses how psychological research can move “from trends to theories” in two distinct ways across two parts. Part I focuses on transforming data trends into robust phenomena within urban mental health research. Although over half of humanity resides in cities, findings remain inconsistent regarding how urban life influences well-being, social satisfaction, and mental health. To address this gap, a continuous measure of urbanicity is introduced that avoids arbitrary boundaries and reveals that UK city living is generally linked to lower and more variable well-being. Further analyses indicate that psychological difficulties accumulate disproportionately among those already struggling, while the last chapter of Part I shows country-specific associations between mental health and urbanicity in Norway, the UK, and New Zealand.Part II turns to systems approaches to shift psychological theories from fleeting trends to lasting scientific contributions, in line with Meehl’s famed observation that ''Theories in soft areas of psychology… tend to neither be refuted nor corroborated, but instead simply fade away as people lose interest.'' As a step in this direction, the thesis first discusses the widely used Ising model and clarifies its dual role: both as a statistical likelihood function and as a system model representing real-world phenomena. Then it broadens this perspective by introducing a comprehensive framework of probabilistic network models, offering practical guidance for simulating, testing, and refining network-based theories. Finally, the thesis advocates a foresighted approach that fosters cumulative theory development, concluding with directions for bridging urban phenomena with the proposed framework network models

European approach to therapy optimization in pediatric asthma:A precision medicine strategy

Asthma is a heterogeneous disease with diverse inflammatory mechanisms and clinical presentations, necessitating a shift from a one-size-fits-all approach towards a precision medicine strategy. This thesis aims to enhance the phenotyping of moderate-to-severe pediatric asthma by identifying potential biomarkers and assessing risk factors to optimize management and treatment.Part 1 provides an overview of pediatric asthma pathophysiology, highlighting recent advances in treatment and the role of biomarker discovery in phenotyping. The impact of emerging technologies, such as digital health tools and omics approaches, in improving precision medicine is also discussed.Part 2 systematically reviews the influence of common risk factors on exhaled volatile organic compounds (VOCs) in obstructive pulmonary diseases, including asthma. Understanding these confounders is crucial for conducting breathomics-based studies.Part 3 investigates pediatric asthma medication use and inhaler device types. The SysPharmPediA study reveals that uncontrolled asthma persists in some children despite adherence to guideline-based treatments. Additionally, ICS inhaler device types (metered-dose inhalers (MDIs) versus dry powder inhalers (DPIs)) are shown to be associated with altered saliva microbiome compositions, suggesting a potential link between inhaler device types and oral microbiome.Part 4 explores blood inflammatory phenotypes using eosinophil and neutrophil counts in pediatric asthma. Four distinct inflammatory phenotypes (eosinophilic, neutrophilic, mixed, and paucigranulocytic) are identified, each associated with different asthma burdens and inflammatory mediator profiles. This phenotyping approach may help and guide the personalized treatment of pediatric asthma.Part 5 integrates all the findings, addresses methodological challenges, and proposes future research directions. This thesis underscores the need for personalized treatment strategies in pediatric asthma to improve clinical outcomes and children's quality of life

Machine learning with generalised symmetries

This thesis explores and expands the rapidly growing field of geometric deep learning, with a particular focus on neural networks that are equivariant to spaces with group actions. Our work is divided into two parts.In Part I, we extend existing approaches by introducing new groups, actions, and architectures. We begin by developing neural networks designed for meshes, which are equivariant to the symmetry of rotations of the bases of the tangent plane. This is followed by the introduction of more scalable equivariant architectures for 3D data, leveraging the power of transformers and geometric algebras. Additionally, we present more efficient samplers for quantum systems that are invariant to symmetries, further broadening the applicability of geometric deep learning techniques. In all cases, we find compelling evidence that the incorporation of symmetries into neural networks improves their performance.Part II of this thesis broadens the scope of geometric deep learning from groups to groupoids. We first apply this concept to causal representation learning, where we identify a groupoid of equivalent models, facilitating the identification of model classes from data. Furthermore, we generalize group equivariance to natural transformations on groupoids, proposing a novel framework we term natural deep learning. We theoretically analyze the space of natural transformations in general, and explore applications to graph-structured data. Finally, we combine our groupoid-based approach with the powerful method of message passing, allowing us to subsume and formalize many previous methods while also inspiring new ones

Transforming HIV prevention:Early adoption of PrEP in Amsterdam

The Amsterdam PrEP demonstration project (AMPrEP) studied oral pre-exposure prophylaxis (PrEP) use among 367 men who have sex with men and transgender women from 2015 to 2020. Participants could opt for daily or event-driven PrEP and switch regimens during quarterly visits. This thesis specifically evaluated adherence, renal safety, sexual behaviour, and STI rates over time.Adherence studies include a randomised trial aiming to improve adherence using a mobile app with visual feedback on PrEP use and sexual behaviour. App feedback did not significantly decrease poor adherence, but did boost excellent adherence. We also identified factors linked to good adherence to PrEP: older age and higher numbers of condomless anal sex with casual partners, while longer PrEP use led to slightly reduced adherence, highlighting the need for continued counselling.Kidney function was monitored over a median of 54 months, showing minor declines consistent with aging, but we did see a slower kidney function decline among event-driven users compared with daily users. All in all, we can recommend reduced monitoring for users at low risk of kidney dysfunction. Long term sexual behaviour and STI outcomes show that, despite high STI incidence, there was no increase in STIs, and STI rates of chlamydia and gonorrhea slightly decreased among daily PrEP users. We also analysed regimen switching and discontinuation. Daily PrEP was preferred, and reasons for switching varied, often linked to age and sexual behaviour. In conclusion, this thesis provides support for wider, low-threshold access to PrEP forms and prioritisation of underserved groups. Community involvement in research and practice is essential for achieving zero new HIV infections

Treatment outcomes in chronic rhinosinusitis

This thesis evaluates the burden and optimal management of patients with chronic rhinosinusitis (CRS), and in particular the medical and surgical treatment options. Chronic rhinosinusitis is a prevalent disease with a 12.8% prevalence in the general population when only symptoms are taken into account, and 3% when symptoms are combined with significant abnormalities at imaging (CT scan of the sinuses with a Lund-Mackay score ≥4). The high prevalence of CRS results in a significant societal burden and high costs, primarily from lost productivity and work absenteeism.In a randomised, controlled, multicentre trial involving 238 patients in 15 centres in the Netherlands, endoscopic sinus surgery combined with medical therapy resulted in a significantly larger improvement of disease-specific health-related quality of life, compared to medical therapy alone. Furthermore, the addition of ESS had a steroid-sparing effect. A systematic review on beneficial and harmful effects of systemic corticosteroids showed a risk of significant adverse events on both the short-term and long-term. Furthermore, the thesis contains a Cochrane Systematic Review on aspirin treatment after desensitization in patients with CRS and NSAID-exacerbated respiratory disease showing significant improvements in disease-specific health-related quality of life and asthma control. Aspirin treatment after desensitization is considered a cheap treatment alternative to revision surgery and/or systemic corticosteroids but carries a risk of adverse events that may lead to frequent discontinuation

From gut feelings to data assets:ethnographic explorations of the gut’s metabolic political economies

The paper contributes to ongoing discussions about the sociopolitical implications of microbiome research reflecting on the “metabolic political economy” of the gut and cautioning against overlooking the complex tensions and ambiguities inherent in the commercialization of microbial science. It investigates the multiple processes underlying the gut’s valuation practices in the context of interactions between academic research and a startup active in the field of wellness. We argue that over the last few decades, the (re)discovery of the gut and its microbiome as a symbiotic, ecological, sensing, and thinking organ has been appropriated and captured by a number of actors. Employing the working framework offered by the concept of “biovalue” and “assetization”, we focus on the wellness and digital health industry, their collaboration with academic research, and the resulting fragmentation of the gut’s valuation practices. Through the ethnographic exploration of a personalized nutrition startup based in the UK and its partnership with a research institution in Italy, we expose how gut microbiome knowledge production takes place at the intersection of multiple and complementary scientific, economic, and health-related values and expectations. Our study unveils a nuanced relationship between data, academic dynamics, and economic drivers, with data playing a determining role in the value acquisition of the gut, well beyond its progressive features. Critically, our analysis emphasizes the necessity to examine the sociopolitical implications and future health management scenarios resulting from the gut’s role as an asset

Politics at your fingertips:The interplay between users and algorithms in online political information search

Search engines have transformed information access by putting a wealth of information, right at our fingertips. While many individuals trust search engines to filter political information for them, the search results encountered may vary substantially from one person to another. The personalised nature of online search raises significant concerns about its implications for democracy, potentially contributing to today’s political crises like polarisation and exposure to misinformation. While much of the scientific and popular debate has focused on the potential of algorithms to create “filter bubbles,” search engines also provide users agency, allowing them to actively shape their information choices. This dissertation offers a novel perspective by putting the crucial yet largely underestimated role of search queries central. It examines how user characteristics, user choices in search queries and algorithmic personalisation shape exposure to political information and news in search results. Furthermore, it explores how innovative computational methods can be employed to study the dynamics between user choices and algorithmic decision-making. Overall, this dissertation contributes to the ongoing debate on the role that users and algorithms play in shaping access to political information in the digital age