Glaucoma is a neurodegenerative disease that affects the retinal ganglion cells (RGCs) and leads to progressive vision loss. The first pathological signs can be seen at the optic nerve head (ONH), the structure where RGC axons leave the retina to compose the optic nerve. Besides damage of the axonal cytoskeleton, axonal transport deficits at the ONH have been described as an important feature of glaucoma. Axonal transport is essential for proper neuronal function, including transport of organelles, synaptic components, vesicles, and neurotrophic factors. Impairment of axonal transport has been related to several neurodegenerative conditions. Studies on axonal transport in glaucoma include analysis in different animal models and in humans, and indicate that its failure happens mainly in the ONH and early in disease progression, preceding axonal and somal degeneration. Thus, a better understanding of the role of axonal transport in glaucoma is not only pivotal to decipher disease mechanisms but could also enable early therapies that might prevent irreversible neuronal damage at an early time point. In this review we present the current evidence of axonal transport impairment in glaucomatous neurodegeneration and summarize the methods employed to evaluate transport in this disease.
Previous VideoGIS integration methods mostly used geographic homography mapping. However, the related processing techniques were mainly for independent cameras and the software architecture was C/S, resulting in large deviations in geographic video mapping for small scenes, a lack of multi-camera video fusion, and difficulty in accessing real-time information with WebGIS. Therefore, we propose real-time web map construction based on the object height and camera posture (RTWM-HP for short). We first consider the constraint of having a similar height for each object by constructing an auxiliary plane and establishing a high-precision homography matrix (HP-HM) between the plane and the map; thus, the accuracy of geographic video mapping can be improved. Then, we map the objects in the multi-camera video with overlapping areas to geographic space and perform the object selection with the multi-camera (OS-CDD) algorithm, which includes the confidence of the object, the distance, and the angle between the objects and the center of the cameras. Further, we use the WebSocket technology to design a hybrid C/S and B/S software framework that is suitable for WebGIS integration. Experiments were carried out based on multi-camera videos and high-precision geospatial data in an office and a parking lot. The case study’s results show the following: (1) The HP-HM method can achieve the high-precision geographic mapping of objects (such as human heads and cars) with multiple cameras; (2) the OS-CDD algorithm can optimize and adjust the positions of the objects in the overlapping area and achieve a better map visualization effect; (3) RTWM-HP can publish real-time maps of objects with multiple cameras, which can be browsed in real time through point layers and hot-spot layers through WebGIS. The methods can be applied to some fields, such as person or car supervision and the flow analysis of customers or traffic passengers.
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