Endothelium—role in regulation of coagulation and inflammation

By its strategic position at the interface between blood and tissues, endothelial cells control blood fluidity and continued tissue perfusion while simultaneously they direct inflammatory cells to areas in need of defense or repair. The endothelial response depends on specific tissue needs and adapts to local stresses. Endothelial cells counteract coagulation by providing tissue factor and thrombin inhibitors and receptors for protein C activation. The receptor PAR-1 is differentially activated by thrombin and the activated protein C/EPCR complex, resulting in antithrombotic and anti-inflammatory effects. Thrombin and vasoactive agents release von Willebrand factor as ultra-large platelet-binding multimers, which are cleaved by ADAMTS13. Platelets can also facilitate leukocyte-endothelium interaction. Platelet activation is prevented by nitric oxide, prostacyclin, and exonucleotidases. Thrombin-cleaved ADAMTS18 induces disintegration of platelet aggregates while tissue-type plasminogen activator initiates fibrinolysis. Fibrin and products of platelets and inflammatory cells modulate the angiogenic response of endothelial cells and contribute to tissue repair

The influence of culture conditions and serum lipids on interleukin-1 production by human monocytes

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Enhancing vehicle destination prediction using latent trajectory information

Intelligent transportation systems have the potential to provide road users with a range of useful applications, including vehicle preconditioning, traffic flow management and intelligent parking recommendations. The majority of these applications can benefit from knowledge of vehicle activities (common situations that a vehicle encounters e.g. traffic), along with the upcoming destinations that a vehicle will visit. We focus on the trajectories that vehicles provide, and the data contained within them, in order to ascertain information about the patterns in individuals' mobility data. Machine learning has been used in many different vehicle applications, and we focus on using these techniques to predict the activity of a vehicle and its future destinations. Clustering methods can be applied at the level of trajectories or the individual instances within them, and we explore both of these alternatives in this thesis. Additionally, we explore several classification approaches to predict activities and destinations. In developing our methods, we make use of a combination of both geospatial and temporal data along with on-board vehicle sensor data. This thesis presents novel methods for filtering stay points to identify points of interest and applying destination prediction to vehicle trajectories. Existing methods for stay point detection are not specific to vehicles, and therefore any region of low mobility is potentially considered to be of interest. We propose a novel method for filtering the extracted stay points to identify points of interest, using vehicle data to predict vehicle activities. The predicted activities are further used to represent trajectories as sequences of annotated locations, to inform the detection of similarities between journeys. Finally, this thesis presents a novel method for using additional properties of a trajectory to cluster trajectories into groupings of similar trajectories with the aim of improving the accuracy of destination prediction. We evaluate our proposed methods on a set of vehicle datasets, varying in purpose and the data available

Role of RhoA and rho kinase in lysophosphatidic acid-induced endothelial barrier dysfunction.

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Improvement of glycaemic control in type 2 diabetes: favourable changes in blood pressure, total cholesterol and triglycerides, but not in HDL cholesterol, fibrinogen, von Willebrand factor and (pro)insulin

Background: Diabetes mellitus causes a substantial increase in cardiovascular risk, which can only partly be reduced by antihyperglycaemic treatment. We were interested in whether improvement in glycaemic control is associated with improvement of other cardiovascular risk factors. Therefore, we studied among type 2 diabetic patients the association between on the one hand changes in glycaemic control and on the other hand within-subject changes of both classic cardiovascular risk factors and less conventional cardiovascular risk indicators that are typically associated with type 2 diabetes (proinsulin, insulin, fibrinogen, von Willebrand factor and the urinary albumin-creatinine ratio). Methods: The 214 type 2 diabetic patients were randomly assigned to either a strict fasting capillary glucose target level (<6.5 mmol/l) or a less strict target (<8.5 mmol/l). Duration of follow-up was two years. Since the interventions did not yield statistically significant differences between the treatment arms, we reanalysed the data focusing on within-subject changes of cardiovascular risk factors and indicators across tertiles of average HbAIC. Results: Individuals in whom HbAIC decreased had significant favourable concurrent changes in triglycerides, total cholesterol, blood pressure, and in the albumin-creatinine ratio in those who were normoalbuminuric at baseline. In contrast, these individuals had unfavourable, although not statistically significant, changes in HDL cholesterol, proinsulin, insulin, fibrinogen and von Willebrand factor. In the whole group, fibrinogen increased more than could be expected on the basis of the relationship between fibrinogen and age, namely from 3.5 ± 0.8 to 3.9 ± 0.9 g/l (p value <0.01). Conclusions: Our results suggest that improvement in glycaemia in type 2 diabetes is associated with significant favourable changes in triglycerides, total cholesterol, blood pressure and, in normoalbuminuric individuals, albumin-creatinine ratio. In contrast, it is not consistently associated with favourable changes in some cardiovascular risk indicators typically associated with diabetes, which may in part explain why antihyperglycaemic treatment does not clearly lower atherothrombotic disease risk

Vehicle point of interest detection using in-car data

Intelligent transportation systems often identify and make use of locations extracted from GPS trajectories to make informed decisions. However, many of the locations identified by existing systems are false positives, such as those in heavy traffic. Signals from the vehicle, such as speed and seatbelt status, can be used to identify these false positives. In this paper, we (i) demonstrate the utility of the Gradient-based Visit Extractor (GVE) in the automotive domain, (ii) propose a classification stage for removing false positives from the location extraction process, and (iii) evaluate the effectiveness of these techniques in a high resolution vehicular dataset