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

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

Classifying vehicle activity to improve point of interest extraction

Knowledge of drivers’ mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data

Anthrax Lethal Toxin-Induced Gene Expression Changes in Mouse Lung

A major virulence factor of Bacillus anthracis is the anthrax Lethal Toxin (LeTx), a bipartite toxin composed of Protective Antigen and Lethal Factor. Systemic administration of LeTx to laboratory animals leads to death associated with vascular leakage and pulmonary edema. In this study, we investigated whether systemic exposure of mice to LeTx would induce gene expression changes associated with vascular/capillary leakage in lung tissue. We observed enhanced susceptibility of A/J mice to death by systemic LeTx administration compared to the C57BL/6 strain. LeTx-induced groups of both up- and down-regulated genes were observed in mouse lungs 6 h after systemic administration of wild type toxin compared to lungs of mice exposed to an inactive mutant form of the toxin. Lungs of the less susceptible C57BL/6 strain showed 80% fewer differentially expressed genes compared to lungs of the more sensitive A/J strain. Expression of genes known to regulate vascular permeability was modulated by LeTx in the lungs of the more susceptible A/J strain. Unexpectedly, the largest set of genes with altered expression was immune specific, characterized by the up-regulation of lymphoid genes and the down-regulation of myeloid genes. Transcripts encoding neutrophil chemoattractants, modulators of tumor regulation and angiogenesis were also differentially expressed in both mouse strains. These studies provide new directions for the investigation of vascular leakage and pulmonary edema induced by anthrax LeTx

Homocysteine-induced cardiomyocyte apoptosis and plasma membrane flip-flop are independent of S-adenosylhomocysteine: a crucial role for nuclear p47(phox).

Item does not contain fulltextWe previously found that homocysteine (Hcy) induced plasma membrane flip-flop, apoptosis, and necrosis in cardiomyocytes. Inactivation of flippase by Hcy induced membrane flip-flop, while apoptosis was induced via a NOX2-dependent mechanism. It has been suggested that S-adenosylhomocysteine (SAH) is the main causative factor in hyperhomocysteinemia (HHC)-induced pathogenesis of cardiovascular disease. Therefore, we evaluated whether the observed cytotoxic effect of Hcy in cardiomyocytes is SAH dependent. Rat cardiomyoblasts (H9c2 cells) were treated under different conditions: (1) non-treated control (1.5 nM intracellular SAH with 2.8 muM extracellular L -Hcy), (2) incubation with 50 muM adenosine-2,3-dialdehyde (ADA resulting in 83.5 nM intracellular SAH, and 1.6 muM extracellular L -Hcy), (3) incubation with 2.5 mM D, L -Hcy (resulting in 68 nM intracellular SAH and 1513 muM extracellular L -Hcy) with or without 10 muM reactive oxygen species (ROS)-inhibitor apocynin, and (4) incubation with 100 nM, 10 muM, and 100 muM SAH. We then determined the effect on annexin V/propodium iodide positivity, flippase activity, caspase-3 activity, intracellular NOX2 and p47(phox) expression and localization, and nuclear ROS production. In contrast to Hcy, ADA did not induce apoptosis, necrosis, or membrane flip-flop. Remarkably, both ADA and Hcy induced a significant increase in nuclear NOX2 expression. However, in contrast to ADA, Hcy additionally induced nuclear p47(phox) expression, increased nuclear ROS production, and inactivated flippase. Incubation with SAH did not have an effect on cell viability, nor on flippase activity, nor on nuclear NOX2-, p47phox expression or nuclear ROS production. HHC-induced membrane flip-flop and apoptosis in cardiomyocytes is due to increased Hcy levels and not primarily related to increased intracellular SAH, which plays a crucial role in nuclear p47(phox) translocation and subsequent ROS production.1 december 201

Substrate stiffening promotes endothelial monolayer disruption through enhanced physical forces

A hallmark of many, sometimes life-threatening, inflammatory diseases and disorders is vascular leakage. The extent and severity of vascular leakage is broadly mediated by the integrity of the endothelial cell (EC) monolayer, which is in turn governed by three major interactions: cell-cell and cell-substrate contacts, soluble mediators, and biomechanical forces. A potentially critical but essentially uninvestigated component mediating these interactions is the stiffness of the substrate to which the endothelial monolayer is adherent. Accordingly, we investigated the extent to which substrate stiffening influences endothelial monolayer disruption and the role of cell-cell and cell-substrate contacts, soluble mediators, and physical forces in that process. Traction force microscopy showed that forces between cell and cell and between cell and substrate were greater on stiffer substrates. On stiffer substrates, these forces were substantially enhanced by a hyperpermeability stimulus (thrombin, 1 U/ml), and gaps formed between cells. On softer substrates, by contrast, these forces were increased far less by thrombin, and gaps did not form between cells. This stiffness-dependent force enhancement was associated with increased Rho kinase activity, whereas inhibition of Rho kinase attenuated baseline forces and lessened thrombin-induced inter-EC gap formation. Our findings demonstrate a central role of physical forces in EC gap formation and highlight a novel physiological mechanism. Integrity of the endothelial monolayer is governed by its physical microenvironment, which in normal circumstances is compliant but during pathology becomes stiffer