Arthropod pests cause significant problems in agricultural crops all around the world. As chemical pesticide use becomes less desired, there is a need for alternative methods of pest control.... Show moreArthropod pests cause significant problems in agricultural crops all around the world. As chemical pesticide use becomes less desired, there is a need for alternative methods of pest control. Inspired by the natural adhesiveness of arthropod trapping plants, we examined the effectiveness of adhesive droplets made from oxidised and cross-linked plant-derived oils for control of western flower thrips. Two filter paper droplet adhesiveness assays and three detached chrysanthemum leaf assays were carried out to test efficacy against thrips. Suspensions containing adhesive droplets and other constituents were applied to filter papers and leaves via spraying or dipping. On filter papers, droplets made from oxidised rice germ oil (RGO) of different sizes caught 40–93% of thrips. Droplets made of a mixture of sunflower, olive, and linseed oil (MIX) caught up to 94% of thrips. Likewise, adhesive droplet-treated filter papers showed higher thrips mortality than untreated or control solution-treated filter papers. On chrysanthemum leaves, thrips were caught by both RGO (up to 40%) and MIX droplets (up to 20%) and thrips damage and reproduction were reduced. On MIX-treated leaves, thrips mortality was also increased. Within treatments, droplets of different size classes occurred and larger droplets were more effective at catching thrips in general. Droplets were also robust to rinsing with water, which is of importance for their application in horticulture. In conclusion, adhesive droplets made from edible plant oils show potential for use in control of western flower thrips. Show less
Lack of knowledge and tools hampers circular transition in the construction industry. This study analyzes the potential of a framework of circular indicators put forward by the Building Research... Show moreLack of knowledge and tools hampers circular transition in the construction industry. This study analyzes the potential of a framework of circular indicators put forward by the Building Research Establishment Environmental Assessment Method (BREEAM-C) as an answer to the prevailing need of a metric for building circularity assessment to promote circular construction. A qualitative analysis approach is adopted, involving literature review, comparative case study and semi-structured interviews conducted for collecting expert opinions. An in-depth scrutiny of the BREEAM-C indicators revealed that they are rooted in circular principles, cover building circularity realizable through circular strategies, and have given due consideration to circularity in different impact areas, structural layers and life-cycle stages of buildings. Moreover, BREEAM-C indicators not only show capacity in identifying CE-related practices implemented, but also serve as benchmarks testifying that CE principles/strategies are incorporated in the design, construction, operation and management of the buildings. Despite having room for expansion, BREEAM-C has proven to be applicable and practical with potential for use in Taiwan as confirmed by expert opinions. Nevertheless, adaptation/localization is required to cater for different concerns with respect to climate and safety as well as local context and legislations. Show less
Quantum computing is an emerging technology, which holds the potential to simulate complex quantum systems beyond the reach of classical numerical methods.Despite recent formidable advancements in... Show moreQuantum computing is an emerging technology, which holds the potential to simulate complex quantum systems beyond the reach of classical numerical methods.Despite recent formidable advancements in quantum hardware, constructing a quantum computer capable of performing useful calculations remains challenging.In the absence of a reliable quantum computer, the study of potential applications relies on mathematical methods, ingenious approximations, and heuristics derived from the fields of application. This thesis focuses on developing new quantum algorithms, targeting some of the key challenges in the simulation of complex quantum systems.The techniques introduced in this thesis span from quantum state preparation to mitigation of hardware and algorithmic noise, from efficient expectation value measurement to noise-resilient applications in quantum chemistry. A common thread connecting all these algorithms is the introduction of a single auxiliary qubit – a fundamental unit of quantum information – which has an active and distinctive role in the task at hand. Show less
Members of the Bacillus genus are widely distributed throughout natural environments and have been studied for decades among others for their physiology, genetics, ecological functions, and... Show moreMembers of the Bacillus genus are widely distributed throughout natural environments and have been studied for decades among others for their physiology, genetics, ecological functions, and applications. However, despite its prevalence in nature, the characterization and classification of Bacillus remain challenging due to its complex and ever-evolving taxonomic framework. This review addresses the current state of the Bacillus taxo- nomic landscape and summarizes the critical points in the development of Bacillus phylogeny. With a clear view of Bacillus phylogeny as a foundation, we subsequently review the methodologies applied in identifying and quantifying Bacillus, while also discussing their respective advantages and disadvantages. Show less
A fundamental understanding of proton transport through graphene nanopores, defects, and vacancies is essential for advancing two-dimensional proton exchange membranes (PEMs). This study employs... Show moreA fundamental understanding of proton transport through graphene nanopores, defects, and vacancies is essential for advancing two-dimensional proton exchange membranes (PEMs). This study employs ReaxFF molecular dynamics, metadynamics, and density functional theory to investigate the enhanced proton transport through a graphene nanopore. Covalently functionalizing the nanopore with a benzenesulfonic group yields consistent improvements in proton permeability, with a lower activation barrier (≈0.15 eV) and increased proton selectivity over sodium cations. The benzenesulfonic functionality acts as a dynamic proton shuttle, establishing a favorable hydrogen-bonding network and an efficient proton transport channel. The model reveals an optimal balance between proton permeability and selectivity, which is essential for effective proton exchange membranes. Notably, the benzenesulfonic-functionalized graphene nanopore system achieves a theoretically estimated proton diffusion coefficient comparable to or higher than the current state-of-the-art PEM, Nafion. Ergo, the benzenesulfonic functionalization of graphene nanopores, firmly holds promise for future graphene-based membrane development in energy conversion devices. Show less
Learning from small data sets in machine learning is a crucial challenge, especially when dealing with data imbalances and anomaly detection. This thesis delves into the challenges and... Show moreLearning from small data sets in machine learning is a crucial challenge, especially when dealing with data imbalances and anomaly detection. This thesis delves into the challenges and methodologies of learning from small datasets in machine learning, with a particular focus on addressing data imbalances and anomaly detec- tion. It thoroughly explores various strategies for effective small dataset learning in ML, examining both existing approaches and introducing novel techniques. The research pivots around two key questions: firstly, it investigates current methods employed for learning from small datasets in machine learning, and secondly, it assesses the efficacy of batch normalization in enhancing model performance and utilizing salient image segmentation as an augmentation policy in self-supervised learning.The thesis comprehensively reviews techniques for managing small datasets, in- cluding data selection and preprocessing, ensemble methods, transfer learning, regularization techniques, and synthetic data generation. A critical examination of batch normalization reveals its significant role in improving training time and testing errors for minority classes in highly imbalanced datasets. The study also demonstrates that utilizing salient image segmentation as an augmentation policy in self-supervised learning substantially improves representation learning. This improvement is particularly evident in the context of downstream tasks such as image segmentation, highlighting the effectiveness of this technique in enhancing model performance.In summary, this study contributes to the field of machine learning by exploring strategies for learning from small datasets. It offers a detailed analysis of batch normalization, highlighting its potential in improving performance for minority classes in imbalanced datasets. Additionally, the study introduces salient image segmentation as an augmentation policy in self-supervised learning, showing its effectiveness in tasks like image segmentation. These findings provide a solid foundation for further research in small sample learning and present practical insights for machine learning practitioners working with limited data. Show less
The research aims to explore the evolutionary adaptability of enzymes and the impact of temperature on protein evolution pathways, using M. tuberculosis β-lactamase BlaC as the object of study.... Show moreThe research aims to explore the evolutionary adaptability of enzymes and the impact of temperature on protein evolution pathways, using M. tuberculosis β-lactamase BlaC as the object of study. Enzymes inherently embody a delicate balance between activity and stability, and the acquisition of new enzymatic functions is often accompanied by trade-offs, such as decreased stability or reduction of the original activity. Probing evolutionary adaptability of BlaC with laboratory evolution in combination with structural characterization can provide information about the mechanisms of rapid adaptations observed for β-lactamases in the clinic. The role of temperature as a conventional selection pressure in such evolutionary adaptation is unclear. The cooperative nature of enzyme unfolding over a narrow temperature trajectory raises the question whether evolution at temperatures well below the melting point is influenced by temperature. The approach used in this work to answer these questions is by simulating evolution under different selection pressures and characterize the variant enzymes in terms of activity, structure, dynamics and melting temperature. The research makes clear how enzyme kinetics and dynamics vary with different selection pressures and maps the evolutionary path that enzymes may take. The underlying structural mechanisms are established to provide a rationale for the observed effects. Show less
We propose polar encoding, a representation of categorical and numerical [0,1]-valued attributes with missing values to be used in a classification context. We argue that this is a good baseline... Show moreWe propose polar encoding, a representation of categorical and numerical [0,1]-valued attributes with missing values to be used in a classification context. We argue that this is a good baseline approach, because it can be used with any classification algorithm, preserves missingness information, is very simple to apply and offers good performance. In particular, unlike the existing missing-indicator approach, it does not require imputation, ensures that missing values are equidistant from non-missing values, and lets decision tree algorithms choose how to split missing values, thereby providing a practical realisation of the "missingness incorporated in attributes" (MIA) proposal. Furthermore, we show that categorical and [0,1]-valued attributes can be viewed as special cases of a single attribute type, corresponding to the classical concept of barycentric coordinates, and that this offers a natural interpretation of polar encoding as a fuzzified form of one-hot encoding. With an experiment based on twenty real-life datasets with missing values, we show that, in terms of the resulting classification performance, polar encoding performs better than the state-of-the-art strategies "multiple imputation by chained equations" (MICE) and "multiple imputation with denoising autoencoders" (MIDAS) and — depending on the classifier — about as well or better than mean/mode imputation with missing-indicators. Show less
The research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency,... Show moreThe research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency, which is evaluated based on donors' hemoglobin and ferritin levels. If either of these levels are inadequate, donors are deferred from donation. Deferral due to low hemoglobin levels occurs on-site, meaning that donors have already traveled to the blood bank and then have to return home without donating, which is demotivating for the donor and inefficient for the blood bank. A large part of this dissertation therefore has the objective to develop a prediction model for donors' hemoglobin levels, based on historical measurements and donor characteristics.The prediction model that was developed reduces the deferral rate by approximately 60\% (from 3\% to 1\% for women, and from 1\% to 0.4\% for men), showing the potential of using data to enhance blood bank policy efficiency. Additionally, the model predictions were made explainable, providing the blood bank with insights into why specific predictions are made. These insights increase our understanding of the relationships between donor characteristics and hemoglobin levels. If this prediction model would be implemented in practice, the explanations could also be shared with the donor to help them understand why they are (not) invited to donate, which could also contribute to donor satisfaction and retention.In a collaborative effort with blood banks in Australia, Belgium, Finland and South Africa, the same prediction model was applied on data from each blood bank. Despite differences in blood bank policies and donor demographics, the models found similar associations with the predictor variables in all countries. Differences in performance could mostly be attributed to differences in deferral rates, with blood banks with higher deferral rates obtaining higher model accuracy.Beyond hemoglobin prediction models, additional research questions are explored. One study aims to identify determinants of ferritin levels in donors through repeated measurements, and linking these to environmental variables. Another study involves modeling the pharmacokinetics of antibodies in COVID-19 recovered donors, and finding relationships between patient characteristics, symptoms, and antibody levels over time.In summary, the research in this dissertation shows the potential within the wealth of data collected by blood banks. The proposed data-driven donation strategies not only decrease deferral rates but also increase donor retention and understanding. This comprehensive approach allows Sanquin to provide more personalised feedback to donors regarding their iron status, ultimately optimising the blood donation process and contributing to the overall efficacy of blood banking systems. Show less
This work describes the use of click-to-release chemistry to get spatiotemporal control over immunocytokine activity. Until now, immunocytokines (cytokines coupled to a tumor-targeting-moiety)... Show moreThis work describes the use of click-to-release chemistry to get spatiotemporal control over immunocytokine activity. Until now, immunocytokines (cytokines coupled to a tumor-targeting-moiety) remained active throughout the body, being able to bind their respective receptors, causing mild to severe side-effects in cancer patients. Attempts have been made to improve the specific action of these immunocytokines, but these solutions remained very cytokine-specific and toxicity was not reduced significantly. Click-to-release chemistry allows us to inactivate a cytokine by blocking its free amines, present in lysines. This prevents the cytokine, IL-1β and TNF-α in particular, from binding its receptor. Removal of the blocking agent using a tetrazine restores the native amine and for IL-1β also its activity. By coupling the blocked cytokine to a targeting moiety allows for transport to the target, the tumor(-environment) upon which the unblocking or decaging can take place. This blocking-unblocking or caging-decaging was assessed using various cell-based assay. This technique can provide new opportunities in the immunocytokine field, as it is not cytokine-specific, and thereby opportunities in cancer therapy development. Show less
Strauss, J.; Wilkinson, C.; Vidilaseris, K.; de Castro Ribeiro Orquidea, M.; Liu, J.; Hillier, J.; ... ; Goldman, A. 2024
Comparative neurobiology allows us to investigate relationships between phylogeny and the brain and understand the evolution of traits. Bats constitute an attractive group of mammalian species for... Show moreComparative neurobiology allows us to investigate relationships between phylogeny and the brain and understand the evolution of traits. Bats constitute an attractive group of mammalian species for comparative studies, given their large diversity in behavioural phenotypes, brain morphology, and array of specialised traits. Currently, the order Chiroptera contains over 1,450 species within 21 families and spans ca. 65 million years of evolution. To date, 194 Neotropical bat species (ca. 13% of the total number of species around the world) have been recorded in Central America. This study includes qualitative and quantitative macromorphological descriptions of the brains of 12 species from six families of Neotropical bats. These analyses, which include histological neuronal staining of two species from different families (Phyllostomus hastatus and Saccopteryx bilineata), show substantial diversity in brain macromorphology including brain shape and size, exposure of mesencephalic regions, and cortical and cerebellar fissure depth. Brain macromorphology can in part be explained by phylogeny as species within the same family are more similar to each other. However, macromorphology cannot be explained by evolutionary time alone as brain differences between some phyllostomid bats are larger than between species from the family Emballonuridae despite being of comparable diverging distances in the phylogenetic tree. This suggests that faster evolutionary changes in brain morphology occurred in phyllostomids — although a larger number of species needs to be studied to confirm this. Our results show the rich diversity in brain morphology that bats provide for comparative and evolutionary studies. Show less
Proton exchange membrane (PEM) water electrolyzers are a promising technology for high-purity, efficient green hydrogen production, with expanding installations. This has increased demand for... Show moreProton exchange membrane (PEM) water electrolyzers are a promising technology for high-purity, efficient green hydrogen production, with expanding installations. This has increased demand for materials like platinum (Pt) used in PEM manufacturing. Conversely, Pt, which currently serves primarily as catalysts for internal combustion engine vehicles (ICEVs), would become available as ICEVs are phased out. Here, we simulate the Pt requirements for rapid scale-up PEM electrolyzers and quantitatively compare these requirements with the availability of Pt from scraped autocatalysts under the IEA-NZE scenario. Our results show that demand for Pt in PEM electrolyzers is expected to increase by an order of magnitude by 2050, while ICEVs are expected to cumulatively scrap ∼2500 tons of Pt. The Pt surplus from ICEVs would meet the increasing Pt demand for PEM eletrolyzers from 2030 onwards. These findings offer fresh insights into using the potential of urban mines to meet the energy transition challenges. Show less
