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Photolatent alkyd curing with iron
Get PDFPaint plays an important part in waterproofing metal and wood, which is vital in extending the lifetime of outdoor structures. Common paints contain several ingredients to control their (physical) properties, such as binders, pigments, fillers and diluents. Traditionally oil paints were used, with plant oils such as linseed oil as binder. Nowadays many of the binders are based on synthetic polymers such as alkyd resins: polyesters prepared by a polycondensation reaction between fatty acids derived from plant oils, a diacid and a polyol. The process of alkyd paint curing (‘drying’) relies on the formation of (conjugated) fatty acid hydroperoxides. Activation of these hydroperoxides by catalysts results in the formation of radical species which (1) can initiate the formation of new peroxide species and (2) can perform reactions leading to crosslinks, resulting in the formation of a three-dimensional network and a cured coating. In commercially available alkyd paints, cobalt-, manganese- and iron-based catalysts are applied frequently, with cobalt-based driers being particularly attractive due to their ability to produce the hardest coatings. Catalysts should however not start curing the paint during storage, and typically (volatile) inhibitors are added to prevent this. Due to legislative pressure regarding the use of cobalt-based driers and certain (volatile) inhibitors in alkyds, these compounds possibly may be banned from used in alkyd formulations in the future. Therefore, suitable alternatives need to be developed. This thesis addresses these issues, and describes new photo-latent catalytic Fe-based driers suitable to provide latent alkyd curing free from (volatile) inhibitors
Plague and piety:Yiddish medical literature in early modern Europe
Get PDFThis dissertation examines responses to epidemics in Yiddish remedy literature within Ashkenazi communities in Western Europe during the long eighteenth century (1660-1800), a period when Yiddish was the vernacular language of all Ashkenazi Jews. It aims to advance scholarly understanding of how Yiddish remedy texts addressed epidemics, a topic previously underexplored as a cohesive entity. By positioning the Yiddish remedy corpus within the broader Jewish remedy literature and early modern European vernacular traditions, this study highlights its significance in the context of medical and religious strategies for managing epidemics.The dissertation builds on Gentilcore’s model of three concentric rings—“medical,” “ecclesiastical,” and “popular”—to analyze how Yiddish remedy literature reflects a complex interplay between these domains. It argues that considerations of piety were integral to all forms of remedy, whether grounded in medical or magical practices. Additionally, the research introduces the concept of “Bio-Political Decrees,” inspired by Foucault, to extend Gentilcore’s model and explore the impact of legislative decisions on public health.The study also critically evaluates Shlomo Berger’s thesis that vernacular publications aimed to enhance religious piety, through an analysis of four case studies: (1) Seyfer Yerum Moyshe (1679), focusing on traditional medical knowledge and piety; (2) Seyfer Refues u’Segules (1703), which blends mystical practices with medical remedies; (3) Seyfer Atsires Hamageyfe (1770), a plague tractate reflecting Jewish-Christian scholarly interactions; and (4) The Maskiel El Dal Manifest (1808), highlighting Dr. Ezechiel Joseph Goldsmit’s advocacy for smallpox vaccination in Amsterdam.The dissertation demonstrates how political and scientific developments from the nineteenth century to the present continue to shape public health strategies. It underscores the ongoing relevance of the intersection between spirituality, medicine, and politics in health discourse, offering insights into its implications for modern public health.<br/
Appointment and delivery rescheduling
Get PDFThis dissertation investigates dynamic scheduling in healthcare and delivery services, focusing on real-time updates to enhance efficiency. The research explores appointment scheduling in healthcare, where schedules are adjusted based on real-time data, such as patient arrival times or congestion in waiting rooms. With modern technologies enabling real-time updates, this thesis investigates dynamic rescheduling, showing that updating schedules based on current information can significantly reduce costs.Through dynamic programming and heuristic methods, the research presents various rescheduling paradigms that optimize the timing and frequency of updates, ensuring minimal disruption to clients while maximizing service efficiency. The methods show that a limited number of well-timed updates can yield substantial benefits. To enhance real-world applicability, the research develops a heuristic that provides real-time schedule updates while accounting for practical constraints. In the domain of last-mile delivery, the dissertation focuses on dynamically updating delivery windows based on real-time driver progress. A method tailored for PostNL was implemented to improve customer experience by narrowing delivery windows throughout the day, allowing for accurate live tracking of parcel arrivals. The research also analyzes driver behavior, revealing deviations from planned routes as a key challenge in accurate scheduling. Using data from Amazon's Last Mile Routing Research Challenge, a method is developed to predict driver routes by learning from past delivery patterns. The integrated approach ranks among the top submissions.The findings have practical applications in healthcare, logistics, and other service industries, contributing to the broader field of operations management by offering innovative solutions for dynamic, real-time scheduling
Ribosome profiling reveals ribosome pausing in <i>Bacillus subtilis</i>
Get PDFThe optimization of protein production focusses on increasing the mRNA levels of the protein of interest, removal of proteases and the induction of protein folding chaperones. However, almost no research has been done on improving translation of the protein of interest, other than simple codon optimization. As far as we know, no ribosome profiling study has been performed focused at the production of industrial relevant enzymes. In Chapter 2, we describe the setting up and testing of the Ribo-seq procedure for B. subtilis, and the investigation of ribosome pausing during the secretion of α-amylase under repeated batch fermentation conditions. The stringent response, which reduces protein translation, is activated in the stationary phase of growth when B. subtilis is most active in the production of secreted enzymes. In Chapter 3, we have investigated how blocking the stringent response affects amylase production and various ribosome profiles. In Chapter 4, we tried to determine whether the ribosome pausing sites found in the amyM mRNA are caused by amino acid sequence motifs or by the nucleotide sequence of the mRNA. In the final experimental chapter, Chapter 5, we have benchmarked several different translation fixation conditions to further optimize Ribo-seq
Towards improved risk prediction of clinical deterioration in acutely ill children in LMICs
Get PDFUnder-five mortality rates have declined globally, yet disparities persist, especially in Sub-Saharan Africa and Southern Asia. Key factors in low- and middle-income countries (LMICs) include malnutrition, acute illnesses, and limited healthcare access. Timely identification of children most at risk for clinical deterioration or mortality remains an important challenge. This thesis explores potential predictive tools, including dermatological assessment, risk prediction models, and fecal volatile organic compound (VOC) analysis. Skin changes were common but not specific to malnutrition. Risk prediction models, used across different hospital settings, including emergency departments, pediatric wards, and pediatric intensive care units were assessed, revealing limitations for use in LMICs. The thesis underscores the need for a multi-model validation approach, stakeholder engagement, and qualitative studies to overcome implementation barriers. Machine learning models can fairly accurately discriminate between mortality and survival in children with an acute illness or complicated severe malnutrition. Larger VOC studies with additional metabolomics could further our understanding of the intestinal dysfunction seen in these children and potentially identify biomarkers. Finally, precision nutrition highlights how personalized treatments based on individual needs and microbial data could improve outcomes in malnourished children. Conclusively, no single prediction model can be recommended for risk prediction in the LMIC setting; specific skin changes were not seen, and VOC analysis demonstrated moderate accuracy in predicting mortality and highlighted potential biomarkers that warrant further research
Evaluating Dutch Speakers and Large Language Models on Standard Dutch:a grammatical Challenge Set based on the <i>Algemene Nederlandse Spraakkunst</i>
No full textThis study evaluates the linguistic knowledge of Dutch Large Language Models (LLMs) by introducing a novel challenge set based on the Algemene Nederlandse Spraakkunst (ANS). The ANS is a comprehensive resource of Dutch prescriptive grammar created by linguists. We collect acceptability judgements of Dutch native speakers on our dataset, validating its usability while observing varying degrees of grammatical acceptability on specific syntactic phenomena. We evaluate both transformer-encoder and transformer-decoder Dutch LLMs on this dataset, and we compare their performance against the standard rules of Dutch in our dataset and the speaker ratings. We find that transformer-encoder models exhibit almost perfect accuracy on our dataset, yet sensitivities for specific sentences differ between models and humans, partially due to mismatches between the reference grammar and actual use of Dutch
Introducing “Corporate Environmental Efficacy” Beliefs:A Stakeholder-Centric Perspective on Strategic Sustainability Communication
No full textOne of the fundamental questions in strategic sustainability communication is how to foster sustainable transformation utilizing purposeful communication. As stakeholders act in the context of their own perceptions of the capability and effectiveness of corporations to tackle the climate crisis, it is vital to approach strategic communication from a perceptual perspective in addition to functional, managerial, and others. This theoretical contribution proposes “corporate environmental efficacy” as a set of beliefs of the capability (corporate self-efficacy, corporate efficacy, and corporate responsiveness efficacy) and effectiveness (corporate response efficacy) of corporate environmental action. Standing on firm interdisciplinary legs, we propose an integrative framework to understand stakeholders’ confidence in sustainable transformation. Key takeaways include a nuanced understanding and operationalization of corporate environmental efficacy, as well as a communication strategy for corporate efficacy. We suggest that further development of an empirical corporate environmental efficacy construct may offer scholars and, not least, practitioners a stakeholder-centric framework to constitute, organize, and evaluate strategic communication for sustainability. In doing so, corporations can effectively engage with stakeholders and reciprocally drive sustainable transformation—moving beyond strategic communication of sustainability to acknowledge that corporate environmental efficacy has the potential to initiate a new cycle of stakeholder-driven and, therefore, more resonating communication
Technical innovations in heart failure and mitral valve disease
Get PDFTransformative changes have substantially improved patient outcomes in the intertwined field of heart failure (HF) and mitral valve regurgitation (MR). Despite these advancements in therapies, challenges remain: medical therapies for HF are highly underutilized worldwide, women are often underrepresented in landmark clinical trials so sex specific analysis are needed, and patient specific models for training or surgical planning in novel interventions warrant additional developments. Part I discusses digital solutions that can improve the usage of guideline directed medical therapies (GDMT). Existing digital solutions to improve GDMT are reviewed and directions for future research are identified. The design and results of the ADMINISTER trial are subsequently described. The ADMINISTER trial showed that optimization using digital consults was effective to improve GDMT within 12 weeks. This can improve healthcare systems by making HF care more efficient, while also improving patient outcomes. Part II focused on technical innovations in mitral valve disease. A sex specific analyses emphasized the high mortality of patients with MR, and identified low GDMT usage as a way to improve patient outcomes, especially in the vulnerable group of males with secondary MR. Developments in fusion and flexible 3D printing for interventional planning are then described. These technical developments allow for improved planning and simulation of these interventions. Future directions include clinical trials designed to test the effectiveness of digital solutions for GDMT optimization on the endpoints of all-cause mortality and hospitalizations as well as trials on whether 3D prints and surgical simulations can improve indicators of surgical performance
Non-coding RNAs in dilated cardiomyopathy
Get PDFDilated cardiomyopathy (DCM) is characterized by left ventricular dilation and impaired systolic function. Progression of the disease leads to heart failure, a state in which the heart is unable to pump sufficient blood to meet the body's metabolic demands. While DCM is often familial and linked to genetic mutations, causative variants are identified in only 30–40% of cases, primarily in protein-coding genes. Given that only 2% of the human genome encodes protein, it is likely that mutations in non-coding regions explain some of the unresolved cases.This thesis investigates the contribution of two classes of non-coding RNAs to DCM pathogenesis. In the first part we focus on microRNAs (miRNAs), ~22 nucleotide long RNAs molecules that regulate gene expression by inhibiting messenger RNA translation. We screened DCM patients for variants in miRNAs implicated in cardiac contractility and identified two variants that alter the precursor miRNA secondary structure. This resulted in reduced miRNA expression and function, potentially contributing to the DCM phenotype in these individuals.The second part explores circular RNAs (circRNAs), covalently closed RNA loops generated by back-splicing. We identified thousands of circRNAs in the human heart, several of which are dysregulated in DCM. Titin, which encodes the largest human transcript, generates the most circRNAs. We show that these TTN-derived circRNAs regulate expression and splicing of key cardiac genes, and that loss of the most abundant TTN-derived circRNA disrupts sarcomere organization and reduces contractility in vitro. Finally, we report a circRNA derived from a well-known cardiac gene as a novel biomarker for heart failure.</p
