Parkinson's disease and gastrointestinal dysfunctions: morphological, neurochemical and functional modifications of the enteric nervous system associated with central dopaminergic impairment

Parkinson’s disease (PD) is frequently associated with gastrointestinal (GI) symptoms, mostly represented by abdominal distension, constipation and defecatory dysfunctions. Despite GI dysfunctions have a major impact on the clinical picture of PD, there is currently a lack of information on the neurochemical, pathological and functional correlates of GI dysmotility associated with PD. Moreover, there is a need of effective and safe pharmacological therapies for managing GI disturbances in PD patients. The present research project has been undertaken to investigate the relationships between PD and related GI dysfunctions by means of investigations in an animal model of PD induced by intranigral injection of 6-hydroxydopamine (6-OHDA). The use of the 6-OHDA experimental model of PD in the present program has allowed to pursue the following goals: 1) to examine the impact of central dopaminergic denervation on colonic excitatory cholinergic and tachykininergic neuromotility by means of molecular, histomorphologic and functional approaches; 2) to elucidate the role of gut inflammation in the onset and progression of colonic dysmotility associated with PD, characterizing the degree of inflammation and oxidative damage in colonic tissues, as well as identifying the immune cells involved in the production of pro-inflammatory cytokines in the gut; 3) to evaluate the impact of chronic treatment with L-DOPA plus benserazide on colonic neuromuscular activity both in control and PD animals. The results suggest that central nigrostriatal dopaminergic denervation is associated with an impaired excitatory cholinergic neurotransmission and an enhanced tachykininergic control, resulting in a dysregulated smooth muscle motor activity, which likely contributes to the concomitant decrease in colonic transit rate. These motor alterations might result from the occurrence of a condition of gut inflammation associated with central intranigral denervation. The treatment with L-DOPA/BE following central dopaminergic neurodegeneration can restore colonic motility, likely through a normalization of the cholinergic enteric neurotransmission, and it can also improve the colonic inflammation associated with central dopaminergic denervation.

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.