3D semantic labeling is a fundamental task in airborne laser scanning (ALS) point clouds processing. The complexity of observed scenes and the irregularity of point distributions make this task quite challenging. Existing methods rely on a large number of features for the LiDAR points and the interaction of neighboring points, but cannot exploit the potential of them. In this paper, a convolutional neural network (CNN) based method that extracts the high-level representation of features is used. A point-based feature image-generation method is proposed that transforms the 3D neighborhood features of a point into a 2D image. First, for each point in the ALS data, the local geometric features, global geometric features and full-waveform features of its neighboring points within a window are extracted and transformed into an image. Then, the feature images are treated as the input of a CNN model for a 3D semantic labeling task. Finally, to allow performance comparisons with existing approaches, we evaluate our framework on the publicly available datasets provided by the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) benchmark tests on 3D labeling. The experiment results achieve 82.3% overall accuracy, which is the best among all considered methods.
Recent interest has been focused on the opioid regulation of heart performance; however, specific allocation of opioid receptors to the parasympathetic, sympathetic, and sensory innervations of the heart is scarce. Therefore, the present study aimed to characterize such specific target sites for opioids in intracardiac ganglia, which act as a complex network for the integration of the heart's neuronal in- and output. Tissue samples from rat heart atria were subjected to RT-PCR, Western blot, radioligand-binding, and double immunofluorescence confocal analysis of mu (M)- and kappa (K)-opioid receptors (ORs) with the neuronal markers vesicular acetylcholine transporter (VAChT), tyrosine hydroxylase (TH), calcitonin gene-related peptide (CGRP), and substance P (SP). Our results demonstrated MOR- and KOR-specific mRNA, receptor protein, and selective membrane ligand binding. By using immunofluorescence confocal microscopy, MOR and KOR immunoreactivity were colocalized with VAChT in large-diameter parasympathetic principal neurons, with TH-immunoreactive small intensely fluorescent (SIF) cells, and on nearby TH-IR varicose terminals. In addition, MOR and KOR immunoreactivity were identified on CGRP- and SP-IR sensory neurons throughout intracardiac ganglia and atrial myocardium. Our findings show that MOR and KOR are expressed as mRNA and translated into specific receptor proteins on cardiac parasympathetic, sympathetic, and sensory neurons as potential binding sites for opioids. Thus, they may well play a role within the complex network for the integration of the heart's neuronal in- and output.
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