Pregnancy-associated cardiac dysfunction and the regulatory role of microRNAs.

Many crucial cardiovascular adaptations occur in the body during pregnancy to ensure successful gestation. Maladaptation of the cardiovascular system during pregnancy can lead to complications that promote cardiac dysfunction and may lead to heart failure (HF). About 12% of pregnancy-related deaths in the USA have been attributed to HF and the detrimental effects of cardiovascular complications on the heart can be long-lasting, pre-disposing the mother to HF later in life. Indeed, cardiovascular complications such as gestational diabetes mellitus, preeclampsia, gestational hypertension, and peripartum cardiomyopathy have been shown to induce cardiac metabolic dysfunction, oxidative stress, fibrosis, apoptosis, and diastolic and systolic dysfunction in the hearts of pregnant women, all of which are hallmarks of HF. The exact etiology and cardiac pathophysiology of pregnancy-related complications is not yet fully deciphered. Furthermore, diagnosis of cardiac dysfunction in pregnancy is often made only after clinical symptoms are already present, thus necessitating the need for novel diagnostic and prognostic biomarkers. Mounting data demonstrates an altered expression of maternal circulating miRNAs during pregnancy affected by cardiovascular complications. Throughout the past decade, miRNAs have become of growing interest as modulators and biomarkers of pathophysiology, diagnosis, and prognosis in cardiac dysfunction. While the association between pregnancy-related cardiovascular complications and cardiac dysfunction or HF is becoming increasingly evident, the roles of miRNA-mediated regulation herein remain poorly understood. Therefore, this review will summarize current reports on pregnancy-related cardiovascular complications that may lead to cardiac dysfunction and HF during and after pregnancy in previously healthy women, with a focus on the pathophysiological role of miRNAs

Subsonic Wing Optimization for Handling Qualities Using ACSYNT

The capability to accurately and rapidly predict aircraft stability derivatives using one comprehensive analysis tool has been created. The PREDAVOR tool has the following capabilities: rapid estimation of stability derivatives using a vortex lattice method, calculation of a longitudinal handling qualities metric, and inherent methodology to optimize a given aircraft configuration for longitudinal handling qualities, including an intuitive graphical interface. The PREDAVOR tool may be applied to both subsonic and supersonic designs, as well as conventional and unconventional, symmetric and asymmetric configurations. The workstation-based tool uses as its model a three-dimensional model of the configuration generated using a computer aided design (CAD) package. The PREDAVOR tool was applied to a Lear Jet Model 23 and the North American XB-70 Valkyrie

Data Driven Waste Management in Smart Cities

Bekreftelse fra programsansvarlig på at det holder kun med engelsk sammendrag. Grunnet masteroppgaven er skrevet på engelsk.Waste management is a critical issue worldwide. One of the major challenges in waste management is the efficient collection and transportation of waste from the source to the disposal facility. Research shows that systematic adoption of data-driven technologies (e.g. Machine Learning and Internet-of-Things) can assist public utilities (Kommune) by a) improving the waste collection management process, and b) minimizing the total incurred cost (Misra et al., 2018; Komninos, 2007). Thus, in this work, we show that systematic adoption of data-driven techniques can significantly improve the waste collection process and minimize the incurred cost to public utilities. In order to perform experiments, we generated a synthetic dataset motivated by a real-life urban environment. Also, we aimed to present different approaches to cost-benefit analysis in the targeted scenario. Our study shows that the systematic use of Internet-of-Things-based smart garbage bins, smart transportation algorithms, and Machine Learning can significantly reduce the total incurred cost of public utilities operating in this space

Post Covid-19 Vaccine (Sinovac) Cerebral Venous Sinus Thrombosis

SINOVAC is an inactivated virus COVID 19 vaccine given emergency authorization for COVID-19 Pandemic. Different adverse reactions have been seen in after-marketing of COVID-19 vaccines. Here we present a case of patient who developed cerebral venous sinus thrombosis two weeks after the first dose of SINOVAC vaccine

Implementation of asymmetric median tree method for the computation of a consensus phylogenetic tree

A phylogenetic tree displays evolutionary relationships between taxonomical units. Nowadays, they are constructed programmatically by using different methods of inference and relying on various data sources, such as DNA and RNA sequences. The resulting evolutionary hypotheses are often contradictory, and need to be resolved by applying consensus methods. In this thesis, asymmetric median tree method (AMT) for construction of consensus phylogenetic trees is described, and along with two other approximation methods implemented in Biopython. AMT was compared to methods of strict, majority and Adams consensus trees on a number of artificial and one real data set. The comparisons were evaluated using the Robinson-Foulds and the tree resolution metrics. The results show that AMT is often the least similar to the input trees. At the same time, it is the most resolved tree, and therefore it offers the most information about evolutionary history of taxonomical units

Implementation of asymmetric median tree method for the computation of a consensus phylogenetic tree

A phylogenetic tree displays evolutionary relationships between taxonomical units. Nowadays, they are constructed programmatically by using different methods of inference and relying on various data sources, such as DNA and RNA sequences. The resulting evolutionary hypotheses are often contradictory, and need to be resolved by applying consensus methods. In this thesis, asymmetric median tree method (AMT) for construction of consensus phylogenetic trees is described, and along with two other approximation methods implemented in Biopython. AMT was compared to methods of strict, majority and Adams consensus trees on a number of artificial and one real data set. The comparisons were evaluated using the Robinson-Foulds and the tree resolution metrics. The results show that AMT is often the least similar to the input trees. At the same time, it is the most resolved tree, and therefore it offers the most information about evolutionary history of taxonomical units

Cerebellar Symptoms After Dengue Fever with Bright Middle Cerebellar Peduncle Sign

Dengue fever is a seasonal epidemic that effects population all across Pakistan every monsoon season and leads to thousands of cases every year. Dengue fever can be associated with neurological complications both during the acute stage and after recovery. These include encephalitis and hemorrhagic complications as well as late immune-related conditions such as Guillain-Barre syndrome and acute disseminated encephalomyelitis. Here we present case of a patient who developed new onset cerebellar symptoms two weeks after recovery from dengue fever, with a middle cerebellar peduncle sign on MRI Brain