Bacteriophages, or phages for short, are the most abundant biological entity in nature. They shape bacterial communities and are a major driving force in bacterial evolution. Their ubiquitous... Show moreBacteriophages, or phages for short, are the most abundant biological entity in nature. They shape bacterial communities and are a major driving force in bacterial evolution. Their ubiquitous nature and their potential use in medical and industrial applications make them attractive targets for fundamental and applied scientific studies. Understanding their structure and function at the molecular level is essential for understanding phage life cycles. In this thesis, I applied different cryo-EM techniques combined with advanced image processing and artificial intelligence methods to gain insight into structure and function of two bacteriophages. In both cases, these phages contain flexible elements which are essential for the infection process. While biologically highly interesting, these flexible components are especially challenging for structural studies. With the advances in computer technology and electron microscopy, researchers can now use various research methods to study different proteins and the structure and function of biological macromolecular machines. The studies presented in this thesis provide valuable insights into phages with flexible components, and provide a useful workflow for researchers with similar research topics. Show less
Transport inspectorates are looking for novel methods to identify dangerous behavior, ultimately to reduce risks associated to the movements of people and goods. We explore a data-driven approach... Show moreTransport inspectorates are looking for novel methods to identify dangerous behavior, ultimately to reduce risks associated to the movements of people and goods. We explore a data-driven approach to arrive at smart inspections of vehicles. Inspections are smart when they are performed (1) accurate, (2) automated, (3) fair, and (4) in an interpretable manner. We leverage tools from the network science and machine learning domain to encode the behavioral aspect of vehicle’s behavior. Tools used in this thesis include community detection, link prediction, and assortativity. We explore their applicability and provide technical methods. In the final chapter, we also discuss the matter of fairness in machine learning. Show less
Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections... Show moreSewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed. Instead of repairing sewer pipes when a problem becomes critical, such inspections allow municipalities to plan maintenance.Sewer pipe inspections are an attractive target for automation. While a potential improvement in terms of assessment quality and processing efficiency is generally promised by automation, in this case we would also decrease the variability which is a current problem. Besides the reasons for automating, the methods for automating are also attractive: a lot of (visual) data has been gathered over the past decades which may be used to train algorithms.This thesis compiles the results of five years of research into the possible automation of sewer pipe inspections with the tools of machine learning and computer vision. In this thesis, three distinct, yet complementary approaches to automating sewer pipe inspections are described:- Image-Based Unsupervised Anomaly Detection- Convolutional Neural Network Classification- Stereovision and Geometry Reconstruction Show less
Novel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel... Show moreNovel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel entities, leading to the formation of aggregated contamination. In this dissertation, we utilized advanced computational methods, such as molecular simulation, data mining, machine learning, and quantitative structure-activity relationship modeling. These methods were used to investigate the mechanisms of interaction between MNPs and other novel entities, the joint toxic action of MNPs and other novel entities, the factors affecting their joint toxicity to ecological species, as well as to quantitatively predict the interaction forces between MNPs and other novel entities, and the toxicity of their mixtures. The results indicate that understanding the mechanisms of interactions between novel entities and their modes of joint toxic action can provide an important theoretical basis for establishing effective risk assessment procedures to mitigate the effects of novel entities on ecosystems and human health. Furthermore, this dissertation provides important technical support and a practical basis for the quantitative prediction of the environmental behavior and toxicological effects of novel entities and their mixtures by applying various advanced in silico methods individually or in combination. Show less
This thesis looks at Artificial Intelligence (AI) and its potential to revolutionise the healthcare sector. The first part of this thesis focuses on the responsible development and validation of AI... Show moreThis thesis looks at Artificial Intelligence (AI) and its potential to revolutionise the healthcare sector. The first part of this thesis focuses on the responsible development and validation of AI-based clinical prediction algorithms, exploring the prime considerations in this process. The second part of this thesis addresses the opportunities for classical statistics and machine learning techniques for developing prediction algorithms. It also examines the performance, potential, and challenges of AI prediction algorithms for clinical practice. The conclusion states that cross-discipline collaboration, exchangeability of knowledge and results, and validation of AI for healthcare practice are essential for realising the potential of AI in healthcare. Show less
In this thesis, we examine various systems through the lens of several numerical methods. We delve into questions concerning thermalization in closed unitary systems, lattice gauge theories, and... Show moreIn this thesis, we examine various systems through the lens of several numerical methods. We delve into questions concerning thermalization in closed unitary systems, lattice gauge theories, and the intriguing properties of deep neural network phase spaces. Leveraging modern advancements in both software and hardware, we scrutinize these systems in greater detail, accessing previously unreachable regimes. Show less
This dissertation investigates the early recognition of persistent somatic symptoms (PSS) in primary care. A stepwise approach was used mapping the optimal methods for re-using primary care records... Show moreThis dissertation investigates the early recognition of persistent somatic symptoms (PSS) in primary care. A stepwise approach was used mapping the optimal methods for re-using primary care records for predictive modeling of PSS. This is important since up to 10% of the general population experiences PSS. Moreover, general practitioners (GPs) often encounter difficulties in recognizing PSS, which may delay adequate intervention, subsequently resulting in unnecessary high burden on the patient and health care system. The findings from this dissertation show that a complex interplay between factors from all biopsychosocial domains contribute to PSS-onset. Survey results show that GPs differ in their methods of PSS-registration. Many GPs indicate missing an unambiguous classification scheme and report needing more support, tools, and/or education for PSS-related consultations. Predictive modeling of different PSS-syndromes shows both overlapping and syndrome-specific predictors. Early predictive modeling of the broad spectrum of PSS shows moderate predictive accuracy based on seven approaches for candidate-predictor selection, including theory-driven and temporal and non-temporal data-driven approaches. In conclusion, this dissertation provides comprehensive evidence of the complexity of identification of PSS. Furthermore, it indicates that simple data-driven approaches could support PSS classification in primary care, although this should be combined with a multidisciplinary care approach. Show less
Radiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the... Show moreRadiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the presence of foreign objects. Computed tomography (CT) enables more accurate visualizations of an object in 3D, but requires more computation time. Spectral X-ray imaging is an important recent development to optimize these conflicting goals of speed and accuracy. This technique enables separation of detected X-ray photons in terms of energy. More information can be extracted from spectral images, which allows for better separation of materials. Deep learning is another important recent technique enabling machines to quickly carry out processing tasks, by training these with large volumes of data for these specific tasks.In this dissertation we present new processing methods that use spectral imaging and machine learning, with a special focus on industrial processes. We design a workflow using CT to efficiently generate large volumes of machine learning training data. In addition, we develop a compression method for efficient processing of large volumes of spectral data and two new spectral CT methods to produce more accurate reconstructions. The presented methods are designed for effective use in industry. Show less
The focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.The goal is develop and assess... Show moreThe focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.The goal is develop and assess the technical methods which are required to shift the actions of the Inspectorate to a data-driven paradigm, concretely under a supervised classification framework of machine learning.The aspect of reliability is addressed as a data quality concern, viz. missingness and noise.The aspect of fairness is addressed as a counter to bias in the selection process of inspections.The conclusion is that, whilst no complete solution has yet been suggested, it is possible to address the concerns related to data quality and data bias, culminating in well-performing classification models which are reliable and fair. Show less
Stroke is one of the leading causes of disability and death worldwide. Prevention of stroke is therefore essential. Effective prevention should be tailored to the clinical characteristics,... Show moreStroke is one of the leading causes of disability and death worldwide. Prevention of stroke is therefore essential. Effective prevention should be tailored to the clinical characteristics, lifestyle, and environment of the individual, among others. This is also known as precision prevention. An important example illustrating the need for precision prevention is the existence of sex differences in stroke occurrence. In practice, for predicting stroke risk, only traditional risk factors (such as smoking and hypertension) are included, and women-specific risk factors are not yet routinely included. As a result, women with an increased risk of stroke may be missed, which also prevents timely initiation of preventive treatments. In this thesis, I tried to lay the foundation for precision prevention of stroke in women.Part I discussed the pathophysiology underlying women-specific risk factors for stroke, and gender differences in the clinical presentation of stroke. I found that the mechanisms underlying the relationship between women-specific risk factors and stroke, in particular the relationship between migraine and cerebral infarctions, seem to be particularly significant in the childbearing phase of life.In Part II, I described how health data from the EHR can be used to develop prediction models for the risk of myocardial infarction or stroke specifically for women under 50 years of age, and found that women-specific risk factors can add value in the predictions. However, there is still a long way to go to actually implement these models in practice, such as testing them on new datasets, and complying with current laws and regulations for safe application. Show less
