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Neuroinflammation is one of the most significant factors involved in the initiation and progression of Parkinson’s disease. PD is a neurodegenerative disorder with a motor disability linked with various complex and diversified risk factors. These factors trigger myriads of cellular and molecular processes, such as misfolding defective proteins, oxidative stress, mitochondrial dysfunction, and neurotoxic substances that induce selective neurodegeneration of dopamine neurons. This neuronal damage activates the neuronal immune system, including glial cells and inflammatory cytokines, to trigger neuroinflammation. The transition of acute to chronic neuroinflammation enhances the susceptibility of inflammation-induced dopaminergic neuron damage, forming a vicious cycle and prompting an individual to PD development. Epigenetic mechanisms recently have been at the forefront of the regulation of neuroinflammatory factors in PD, proposing a new dawn for breaking this vicious cycle. This review examined the core epigenetic mechanisms involved in the activation and phenotypic transformation of glial cells mediated neuroinflammation in PD. We found that epigenetic mechanisms do not work independently, despite being coordinated with each other to activate neuroinflammatory pathways. In this regard, we attempted to find the synergic correlation and contribution of these epigenetic modifications with various neuroinflammatory pathways to broaden the canvas of underlying pathological mechanisms involved in PD development. Moreover, this study highlighted the dual characteristics (neuroprotective/neurotoxic) of these epigenetic marks, which may counteract PD pathogenesis and make them potential candidates for devising future PD diagnosis and treatment.

Abstract:This paper presents a hierarchical simultaneous localization and mapping (SLAM) system for a small unmanned aerial vehicle (UAV) using the output of an inertial measurement unit (IMU) and the bearing-only observations from an onboard monocular camera. A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match. This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter (EKF) for attitude and velocity estimation. Then, another EKF is employed to estimate the position of the vehicle and the locations of the features in the map. Both simulations and experiments are carried out to test the performance of the proposed system. The result of the comparison with the referential global positioning system/inertial navigation system (GPS/INS) navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.