MOLECULAR EPIDEMIOLOGY OF STENOTROPHOMONAS MALTOPHILIA (SM) IN THE CYSTIC FIBROSIS (CF) CENTRE OF GENOVA

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Pseudomonas aeruginosa (PA) resistant to colistin (CL) in Italian cystic fibrosis (CF) patients

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STAPHYLOCOCCUS AUREUS (SA) SMALL COLONY VARIANT (SCV) IN FIBROSI CISTICA (FC).

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Coordinate and redox interactions of epinephrine with ferric and ferrous iron at physiological pH

Coordinate and redox interactions of epinephrine (Epi) with iron at physiological pH are essential for understanding two very different phenomena - the detrimental effects of chronic stress on the cardiovascular system and the cross-linking of catecholamine-rich biopolymers and frameworks. Here we show that Epi and Fe3+ form stable high-spin complexes in the 1:1 or 3:1 stoichiometry, depending on the Epi/Fe3+ concentration ratio (low or high). Oxygen atoms on the catechol ring represent the sites of coordinate bond formation within physiologically relevant bidentate 1:1 complex. Redox properties of Epi are slightly impacted by Fe3+. On the other hand, Epi and Fe2+ form a complex that acts as a strong reducing agent, which leads to the production of hydrogen peroxide via O-2 reduction, and to a facilitated formation of the Epi-Fe3+ complexes. Epi is not oxidized in this process, i.e. Fe2+ is not an electron shuttle, but the electron donor. Epi-catalyzed oxidation of Fe2+ represents a plausible chemical basis of stress-related damage to heart cells. In addition, our results support the previous findings on the interactions of catecholamine moieties in polymers with iron and provide a novel strategy for improving the efficiency of cross-linking.Supplementary material: [http://cherry.chem.bg.ac.rs/handle/123456789/3040

Extended Object Tracking in Curvilinear Road Coordinates for Autonomous Driving

In literature, Extended Object Tracking (EOT) algorithms developed for autonomous driving predominantly provide obstacles state estimation in cartesian coordinates in the Vehicle Reference Frame. However, in many scenarios, state representation in road-aligned curvilinear coordinates is preferred when implementing autonomous driving subsystems like cruise control, lane-keeping assist, platooning, etc. This paper proposes a Gaussian Mixture Probability Hypothesis Density~(GM-PHD) filter with an Unscented Kalman Filter~(UKF) estimator that provides obstacle state estimates in curvilinear road coordinates. We employ a hybrid sensor fusion architecture between Lidar and Radar sensors to obtain rich measurement point representations for EOT. The measurement model for the UKF estimator is developed with the integration of coordinate conversion from curvilinear road coordinates to cartesian coordinates by using cubic hermit spline road model. The proposed algorithm is validated through Matlab Driving Scenario Designer simulation and experimental data collected at Monza Eni Circuit

An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving

Understanding of the driving scenario represents a necessary condition for autonomous driving. Within the control routine of an autonomous vehicle, it represents the preliminary step for the motion planning system. Estimation algorithms hence need to handle a considerable number of information coming from multiple sensors, to provide estimates regarding the motion of ego-vehicle and surrounding obstacles. Furthermore, tracking is crucial in obstacles state estimation, because it ensures obstacles recognition during time. This paper presents an integrated algorithm for the estimation of ego-vehicle and obstacles’ positioning and motion along a given road, modeled in curvilinear coordinates. Sensor fusion deals with information coming from two Radars and a Lidar to identify and track obstacles. The algorithm has been validated through experimental tests carried on a prototype of an autonomous vehicle

Fault Resistant Odometry Estimation using Message Passing Neural Network

Multi-modal sensor fusion constitutes an essential ingredient for safe autonomous navigation. In the last years, many works have improved the accuracy of Deep-Learning-based odometry estimators. However, the robustness of these algorithms to sensor failure or measurement degradation, which are very likely to happen during navigation, has been studied less extensively. Furthermore, works studying the robustness of the fusion modules are developed without modeling the correlation between sensor features, which is crucial to filter out features derived from noisy measurements and in sensor faults scenarios. To bridge this gap, in this paper, we propose a fault-resistant odometry estimator, which produces robust estimates even when the sensors completely fail, or measurements progressively degrade. Our framework models the correlation between the sensor embedding using Message Passing Neural Network (MPNN), a particular type of Graph Neural Network (GNN). A mask is then computed from the updated node features of the graph to weigh the multi-modal features computed from different sensors. We evaluate the proposed fusion strategy on the modified raw KITTI dataset with sensor degradation scenarios. Finally, we compare against state-of-the-art baselines based on trivial features concatenation and soft-fusion to demonstrate our method's superiority in terms of accuracy and robustness to sensor degradation and failures