Serum outperforms plasma in small extracellular vesicle microRNA biomarker studies of adenocarcinoma of the esophagus

Background: To compare computed tomography coronary angiography (CTCA) with intravascular ultrasound (IVUS) in quantitative and qualitative plaque assessment. Methods: Patients who underwent IVUS and CTCA within 3 months for suspected coronary artery disease were retrospectively studied. Plaque volumes on CTCA were quantified manually and with automated-software and were compared to IVUS. High-risk plaque features were compared between CTCA and IVUS. Results: There were 769 slices in 32 vessels (27 patients). Manual plaque quantification on CTCA was comparable to IVUS per slice (mean difference of 0.06 ± 0.07, p = 0.44; Bland-Altman 95% limits of agreement -2.19–2.08 mm3, bias of -0.06 mm3) and per vessel (3.1 mm3 ± -2.85 mm3, p = 0.92). In contrast, there was significant difference between automated-software and IVUS per slice (2.3 ± 0.09mm3, p < 0.001; 95% LoA -6.78 to 2.25 mm3, bias of -2.2 mm3) and per vessel (33.04 ± 10.3 mm3, p < 0.01). The sensitivity, specificity, positive and negative predictive value of CTCA to detect plaques that had features of echo-attenuation on IVUS was 93.3%, 99.6%, 93.3% and 99.6% respectively. The association of ≥2 high-risk plaque features on CTCA with echo attenuation (EA) plaque features on IVUS was excellent (86.7%, 99.6%, 92.9% and 99.2%). In comparison, the association of high-risk plaque features on CTCA and plaques with echo-lucency on IVUS was only modest. Conclusion: Plaque volume quantification by manual CTCA method is accurate when compared to IVUS. The presence of at least two high-risk plaque features on CTCA is associated with plaque features of echo attenuation on IVUS.Ravi Kiran Munnur, Jordan Andrews ... Dorothy Keefe ... Lorelle Smith ... Joanne Bowen ... Sarah Thompson ... et al

A vector quantization approach to scenario generation for stochastic NMPC

This paper describes a novel technique for scenario generation aimed at closed loop stochastic nonlinear model predictive control. The key ingredient in the algorithm is the use of vector quantization methods. We also show how one can impose a tree structure on the resulting scenarios. Finally, we briefly describe how the scenarios can be used in large scale stochastic nonlinear model predictive control problems and we illustrate by a specific problem related to optimal mine planning

Model Predictive Control of Vehicle Formations

We propose a two-layer scheme to control a set of vehicles moving in a formation. The first; layer, file trajectory controller, is a nonlinear controller since most vehicles are nonholonomic systems and require a nonlinear, even discontinuous, feedback to stabilize them. The trajectory controller, a model predictive controller, computes centrally a bang-bang control law and only a small set of parameters need to be transmitted to each vehicle at each iteration. The second layer, the formation controller, aims to compensate for small changes around a nominal trajectory maintaining the relative positions between vehicles. We argue that; the formation control call be, in most; cases, adequately carried out, by a linear model predictive controller accommodating input, and state constraints. This has the advantage that the control laws for each vehicle are simple piecewise affine feedback laws that, call be pre-computed off-line and implemented in a, distributed way in each vehicle. Although several optimization problems have to be solved, the control strategy proposed results in a simple and efficient; implementation where no optimization problem needs to be solved in real-time at each vehicle