Spontaneous right ventricular pseudoaneurysms and increased arrhythmogenicity in a mouse model of Marfan syndrome

Patients with Marfan syndrome (MFS), a connective tissue disorder caused by pathogenic variants in the gene encoding the extracellular matrix protein fibrillin-1, have an increased prevalence of primary cardiomyopathy, arrhythmias, and sudden cardiac death. We have performed an in-depth in vivo and ex vivo study of the cardiac phenotype of Fbn1mgR/mgR mice, an established mouse model of MFS with a severely reduced expression of fibrillin-1. Using ultrasound measurements, we confirmed the presence of aortic dilatation and observed cardiac diastolic dysfunction in male Fbn1mgR/mgR mice. Upon post-mortem examination, we discovered that the mutant mice consistently presented myocardial lesions at the level of the right ventricular free wall, which we characterized as spontaneous pseudoaneurysms. Histological investigation demonstrated a decrease in myocardial compaction in the MFS mouse model. Furthermore, continuous 24 h electrocardiographic analysis showed a decreased heart rate variability and an increased prevalence of extrasystolic arrhythmic events in Fbn1mgR/mgR mice compared to wild-type littermates. Taken together, in this paper we document a previously unreported cardiac phenotype in the Fbn1mgR/mgR MFS mouse model and provide a detailed characterization of the cardiac dysfunction and rhythm disorders which are caused by fibrillin-1 deficiency. These findings highlight the wide spectrum of cardiac manifestations of MFS, which might have implications for patient care

Transactive Control of Coupled Electric Power and District Heating Networks

The aim to decarbonize the energy supply represents a major technical and social challenge. The design of approaches for future energy network operation faces the technical challenge of needing to coordinate a vast number of new network participants spatially and temporally, in order to balance energy supply and demand, while achieving secure network operation. At the same time these approaches should ideally provide economic optimal solutions. In order to meet this challenge, the research field of transactive control emerged, which is based on an appropriate interaction of market and control mechanisms. These approaches have been extensively studied for electric power networks. In order to account for the strong differences between the operation of electric power networks and other energy networks, new approaches need to be developed. Therefore, within this work a new transactive control approach for Coupled Electric Power and District Heating Networks (CEPDHNs) is presented. As this is built upon a model-based control approach, a suitable model is designed first, which enables to operate coupled electric power and district heating networks as efficient as possible. Also, for the transactive control approach a new fitted procedure is developed to determine market clearing prices in the multi-energy system. Further, a distributed form of district heating network operation is designed in this context. The effectiveness of the presented approach is analyzed in multiple simulations, based on real world networks

The 1992 Goddard Conference on Space Applications of Artificial Intelligence

The purpose of this conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers fall into the following areas: planning and scheduling, control, fault monitoring/diagnosis and recovery, information management, tools, neural networks, and miscellaneous applications

The development of psychiatric disorders and adverse behaviors : from context to prediction

Psychiatric disorders by definition cause significant impairment in an individual’s daily functioning. Certain disorders, such as borderline personality disorder (BPD) and eating disorders, have worse prognosis and high mortality rates compared to other psychiatric disorders. Similarly, adverse behaviors such as self-harm, suicide, and crime are often present in individuals with psychiatric disorders. It is of interest to further understand the etiology and associations of BPD and eating disorders to uncover potential avenues and opportunities for intervention. Moreover, prediction modeling has recently come of interest to psychiatric epidemiologists with the rise of large data sets. Prediction modeling may provide valuable information about the nature of risk factors and eventually aid clinical diagnostics and prognostics. Thus, the studies included in this thesis seek to examine the etiology, associations, and prediction approaches of psychiatric disorders and adverse behaviors. Study I examined the individual and familial association between type 1 diabetes (T1D) and eating disorder diagnoses. We used national health care records from Denmark (n = 1,825,920) and Sweden (n = 2,517,277) to calculate the association within individuals, full siblings, half siblings, full cousins, and half cousins. Individuals with T1D had twice the hazard rate ratio of being diagnosed with an eating disorder compared to the general population. There was conflicting evidence for the risk of an eating disorder in full siblings of T1D patients. However, there was no evidence to support a further familial relationship between the two conditions. Study II aimed to illuminate the nature of the correlates for BPD across time, sex, and for their full siblings. We examined 87 variables across psychiatric disorders, somatic illnesses, trauma, and adverse behaviors (such as self-harm). In a sample of 1,969,839 Swedes with 12,175 individuals diagnosed with BPD, we found that BPD was associated with nearly all of the examined variables. The associations were largely consistent across time and between the sexes. Finally, we found that having a sibling diagnosed with BPD was associated with psychiatric disorders, trauma, and adverse behaviors but not somatic illnesses. Study III created a prediction model that could predict who would have high or low psychiatric symptoms at age 15 based on data from parental reports and national health care registers collected at age 9 or 12. Additionally, we compared multiple types of machine learning algorithms to assess predictive performance. The sample included 7,638 twins from the Child and Adolescent Twin Study in Sweden (CATSS). Our model was able to predict the outcome with reasonable performance but is not suitable for use in clinics. Each model performed similarly indicating that researchers with similar data and research questions do not need to forgo standard logistic regression. Study IV aimed to determine if an individual will exhibit suicidal behaviour (self-harm or suicidal thoughts), aggressive behaviour, both, or neither before adulthood with prediction modeling. Through variable importance scores we examined the usefulness of genetic variables within the model. A total of 5,974 participants from CATSS and 2,702 participants from the Netherlands Twin Register (NTR) were included in the study. The model had adequate performance in both the CATSS and NTR datasets for all classes except for the suicidal behaviors class in the NTR, which did not perform better than chance. The included genetic data had higher variable importance scores than questionnaire data completed at age 9 or 12, indicating that genetic biomarkers can be useful when combined with other data types. In conclusion, the development of psychiatric disorders and symptoms are associated with many factors across somatic illnesses, other psychiatric disorders, trauma, and harmful behaviors. The results of this thesis demonstrates the limitations of prediction modeling in psychiatric clinics but highlights their use in research and on the path forward towards personalized medicine

Modeling genetic susceptibility to multiple sclerosis

The main aim of this thesis was to investigate genetic and environmental factors and their role in the etiology of Multiple Sclerosis (MS) by using comprehensive registry data or novel computationally intense methods. To date, over 100 genes associated with MS have been identified, but how they interact in the risk for the disease is not yet fully understood. The presence of high prevalence clusters has led researchers to believe that there might be as yet unidentified rare variant involved in the disease etiology. In Paper I, we attempted to search for these rare variants by using a population based linkage approach, estimating haplotypes shared between individuals inherited by descent from some common ancestor. One significant hit was found on chromosome 19, but due to methodological problems the result should be interpreted with caution. MS is commonly attributed high familial risks, decreasing with relatedness, which indicates a large genetic component involved in the disease etiology. In Paper II, nationwide registry data was used to reinvestigate the familial risks and estimate the proportion of genetics and environment contributing to disease etiology. The relative risks estimated were lower than usually reported, with a sibling relative risk of 7.1 and no significant differences between the sexes. The heritability was estimated to be 64% and the environmental 36% with a non-significant shared environmental component of 1%. In Paper III, the women-to-men ratio for MS in Sweden was reinvestigated. MS is a disease more common in women than men, and an increase in the women-to-men ratio has been reported in several countries. However, a report from Sweden did not show this increase in women and Paper III extended this report using data from nationwide registers. An increase among women compared to men was identified, and when comparing against the previous study, an inclusion bias, presumably caused by a higher mortality rate among the oldest men, was identified. One framework used to model complex diseases such as MS is the sufficent cause model, also known as Rothman's pie model. This model hypotehsizes that a disease can be caused by several mechanisms, or pies, each consisting of a set of different factors and when all factors are present they will inevitably cause disease. Paper IV extends this model into a stochastic version and presents an algorithm that can estimate the probability that an a priori suggested mechanism has caused disease in a certain individual. The algorithm showed high classification accuracy on synthetic data; however it needs further investigation of its properties. In conclusion, this thesis revise the familial risks for MS to more moderate levels, with no differences between the sexes, and confirms the global trend of an increasing women-to-men ratio. No rare variants contributing to MS on population level were identified. We also present a probabilitic version of Rothman's pie model, showing promising results on synthetic data

Population Genomics of Selection in the Eastern Oyster Contact Zone

Intraspecific clinal systems are ideal for investigating the how divergence occurs in the presence of gene flow because they represent a balance between selection and gene flow prior to speciation. High dispersal marine species with clinal variation are particularly informative to test for divergent selection because selection likely is strong enough to counteract high gene flow. The degree of population structure varies considerably among loci, such that the genome acts as a sieve allowing gene flow at neutral loci and impeding it at selected loci, creating a genomic mosaic of differentiation. In this study, I examine genomic and geographic patterns of differentiation among parapatric populations of the eastern oyster (Crassostrea virginica) along their contact zone in Florida estuaries. The planktotrophic larval phase of this species gives it the potential for regular long-distance dispersal and genetically homogeneous populations. However Florida populations at the center of its range exhibit a sharp step cline at some loci, suggesting a role for divergent selection. Using 217 AFLP loci, including seven candidate loci for differential selection between the two populations, I genotyped 1,011 spat over two seasons and 274 adults at sites along the contact zone. I examined: (1) whether genome scans can detect divergent selection in a clinal system, (2) the genomic and geographic patterns of differentiation along the cline at neutral and selected loci, and (3) regional patterns of differentiation and genotypic distributions among the life stages. Results demonstrated: (1) candidate loci for regionally divergent selection, (2) a genomic and geographic mosaic of differentiation, (3) regional and localized selection at a non-trivial portion of loci, (4) lower recruitment and some mortality in the center of the cline, and (5) strong exogenous, post-settlement viability selection against intermediate and non-native-like genotypes. While a combination of neutral and adaptive processes likely shape genomic and geographic patterns of differentiation, this study revealed evidence for divergent selection in an estuarine species with high potential for gene flow. Overall, these results point to a major role for post-zygotic, environment-dependent selection in the maintenance of the contact zone between Atlantic and Gulf-type oyster populations