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Left ventricular thrombus (LVT) is associated with a significant risk of ischemic stroke (IS) and peripheral embolization. Societal guidelines recommend the use of warfarin, with direct oral anticoagulants (DOACs) only for patients unable to tolerate warfarin. We studied the natural history of LVT with anticoagulation (AC) with emphasis on comparing warfarin and DOAC use. In this single center study, we identified patients with a confirmed LVT. Type and duration of anticoagulation, INR levels and clinical outcomes (bleeding, ischemic stroke or peripheral embolization, and thrombus resolution) were recorded. LVT was confirmed in a total of 110 patients. Mean age was 59 + 14 years. 79% were men. Underlying etiology was chronic ischemic cardiomyopathy in 58%, non-ischemic cardiomyopathy in 23%. AC was started in 96 (87%) patients. At 1 year follow up, 11 patients (10%) had a stroke while on any AC (2 had hemorrhagic stroke and 9 had IS). Of those with IS, 7 were on warfarin (71% of those had subtherapeutic INR) and 2 patients on DOACs had IS. The 1-year risk of any stroke was 15% in warfarin group (12% risk of ischemic stroke) compared to 6% in the DOACs group (p = 0.33). 37 (63%) patients on warfarin and 18 (53%) on DOACs had resolution of thrombus (p = 0.85). One-year risk of stroke with LVT is high (10%) even with AC. Most patients IS on warfarin had subtherapeutic INR. There was no statistical difference in stroke risk or rate of thrombus resolution between warfarin and DOACs treated patients.

Cocaine use causes significant cardiovascular morbidity from its hemodynamic effects. It is less clear whether cocaine promotes atherosclerosis. Vascular inflammation is one of the earliest steps in the pathophysiology of atherosclerosis. We hypothesized that cocaine results in an increase in inflammatory markers. Study objective was to measure the acute effects of intravenous cocaine on biomarkers of vascular inflammation. Eleven chronic cocaine users were enrolled. After a drug-free period, they received intravenous cocaine at 0.36 mg/kg dose in an in-hospital controlled environment. Serum levels of soluble CD40 ligand, monocyte chemoattractant protein-1, interleukin 6, and soluble intercellular adhesion molecule-1 were measured at baseline, 6 h, 24 h, and 6 days after cocaine challenge and at baseline for controls. After cocaine challenge, sCD40 ligand levels decreased in subjects and were significantly lower at 24 h. MCP-1 levels decreased and were significantly lower at the 6-day time point. No significant changes in IL-6 or sICAM-1 level were found. In conclusion, intravenous cocaine did not result in an increase in levels of inflammatory markers. Levels of MCP-1 and sCD40L decreased significantly. This unexpected finding suggests that chronic effects of cocaine on inflammation may be different from acute effects or that higher dosing may have differential effects as compared to lower dose used here.

We present efficient localization aware sampling and connection strategies for incremental sampling-based stochastic motion planners. For sampling, we introduce a new measure of localization ability of a sample, one that is independent of the path taken to reach the sample and depends only on the sensor measurement at the sample. Using this measure, our sampling strategy puts more samples in regions where sensor data is able to achieve higher uncertainty reduction while maintaining adequate samples in regions where uncertainty reduction is poor. This leads to a less dense roadmap and hence results in significant time savings. We also show that a stochastic planner that uses our sampling strategy is probabilistically complete under some reasonable conditions on parameters. We then present a localization aware efficient connection strategy that uses an uncertainty aware approach in connecting the new sample to the neighbouring nodes, i.e., it uses an uncertainty measure (as opposed to distance) to connect the new sample to a neighboring node so that the new sample is reachable with least uncertainty ("the closest"), and furthermore, connections to other neighbouring nodes are made only if the new path to them (via the new sample) helps to reduce the uncertainty at those nodes. This is in contrast to current incremental stochastic motion planners that simply connect the new sample to all of the neighbouring nodes and therefore, take more search queue iterations to update the paths (i.e., uncertainty propagation). Hence, our efficient connection strategy, in addition to eliminating the inefficient edges that do not contribute to better localization, also reduces the number of search queue iterations. We provide simulation results that show that (a) our localization aware sampling strategy places less samples and find a well-localized path in shorter time with little compromise on the quality of path as compared to existing sampling techniques, (b) our localization aware connection strategy finds a well-localized path in shorter time with no compromise on the quality of path as compared to existing connection techniques, and finally (c) combined use of our sampling and connection strategies further reduces the planner run time.

We present a hierarchical and adaptive mobile manipulator planner (HAMP) that plans for both the base and the arm in a judicious manner--allowing the manipulator to change its configuration autonomously when needed if the current arm configuration is in collision with the environment as the mobile manipulator moves along the planned path. This is in contrast to current implemented approaches that are conservative and fold the arm into a fixed home configuration. Our planner first constructs a base roadmap and then for each node in the roadmap it checks for collision status of current manipulator configuration along the edges formed with adjacent nodes, if the current manipulator configuration is in collision, the manipulator C-space is searched for a new reachable configuration such that it is collision-free as the mobile manipulator moves along the edge and a path from current configuration to the new reachable configuration is computed. We show that HAMP is probabilistically complete. We compared HAMP with full 9D PRM and observed that the full 9D PRM is outperformed by HAMP in each of the performance criteria, i.e., computational time, percentage of successful attempts, base path length, and most importantly, undesired motions of the arm. We also evaluated the tree versions of HAMP, with RRT and bi-directional RRT as core underlying sub-planners, and observed similar advantages, although the time saving for bi-directional RRT version is modest. We then present an extension of HAMP (we call it HAMP-U) that uses belief space planning to account for localization uncertainty associated with the mobile base position and ensures that the resultant path for the mobile manipulator has low uncertainty at the goal. Our experimental results show that the paths generated by HAMP-U are less likely to result in collision and are safer to execute than those generated by HAMP (without incorporating uncertainty), thereby showing the importance of incorporating base pose uncertainty in our overall HAMP algorithm.

We present a parallel formulation for enumerative search in high dimensional spaces and apply it to planning paths for a 6-dof manipulator robot. Participating processors perform local A* search towards the goal configuration. To exploit all the processors at their maximum capacity at all times, a dynamic load-balancing scheme matches idle and busy processors for load transfer. For comparison purposes, we have also implemented an existing parallel static load-balancing formulation based on regular domain decomposition. Both methods achieved almost linear speed-up in our experiments. The two methods follow different search strategies in parallel and the implementation of the existing method (with tuned space decomposition) was more time efficient on average. However, the planning time of that method is highly dependent on the distribution of the search space among the processors and its tuned decomposition varies for different obstacle placements. Empirical selection of the space decomposition parameters for the existing method does not guarantee minimal planning time in all environments and leads to slower planning than our dynamic load-balancing method in some cases. The performance of the developed dynamic method is independent of the obstacle placements and the method can achieve consistent speed-up in all environments.

Previous research has suggested that consumers use multiple strands to evaluate their satisfaction with a product and to establish postpurchase behavioral intention. However, prior empirical research has focused on which individual standard best predicts satisfaction. In contrast, this article develops and tests a model of consumer satisfaction and postpurchase behavioral intention in which consumers simultaneously use multiple standards—perceptions of performance, brand expectations, and category expectations. The results of an experiment for a simulated service encounter provide support for the proposition that consumers use multiple standards and that these standards have differential effects on such postpurchase outcomes as satisfaction, repurchase intention, and willingness to recommend. Brand expectations are shown to be better predictors of affective outcomes (such as satisfaction), while category expectations are shown to be better predictors of behavioral outcomes (repurchase and recommendation). Consistent with prior research, perceived performance is shown to have a strong effect on both satisfaction and behavioral intention.