• Amylin mediates brainstem control of heart rate in the diving reflex

      Dun, Nae J.; Cowan, Alan, 1942-; Liu-Chen, Lee-Yuan; Brailoiu, Gabriela C.; Chong, Parkson Lee-Gau; Sapru, Hreday N. (Temple University. Libraries, 2012)
      Amylin, or islet amyloid polypeptide is a 37-amino acid member of the calcitonin peptide family. Amylin role in the brainstem and its function in regulating heart rates is unknown. The diving reflex is a powerful autonomic reflex, however no neuropeptides have been described to modulate its function. In this thesis study, amylin expression in the brainstem involving pathways between the trigeminal ganglion and the nucleus ambiguus was visualized and characterized using immunohistochemistry. Its functional role in slowing heart rate and also its involvement in the diving reflex were elucidated using stereotaxic microinjection, whole-cel patch-clamp, and a rat diving model. Immunohistochemical and tract tracing studies in rats revealed amylin expression in trigeminal ganglion cells, which also contained vesicular glutamate transporter 2 positive. With respect to the brainstem, amylin containing fibers were discovered in spinal trigeminal tracts. These fibers curved dorsally toward choline acetyltransferase immunoreactive neurons of the nucleus ambiguus, suggesting that amylin may synapse to parasympathetic preganglionic neurons in the nucleus ambiguus. Microinjection of fluorogold to the nucleus ambiguus retrogradely labeled a population of trigeminal ganglion neurons; some of which also contained amylin. In urethane-anesthetized rats, stereotaxic microinjections of amylin to the nucleus ambiguus caused a dose-dependent bradycardia that was reversibly attenuated by microinjections of the selective amylin receptor antagonist, salmon calcitonin (8-32) (sCT (8-32)) or AC187, and abolished by bilateral vagotomy. In an anesthetized rat diving model, diving bradycardia was attenuated by glutamate receptor antagonists CNQX and AP5, and was further suppressed by AC187. Whole-cel patch-clamp recordings from cardiac preganglionic vagal neurons revealed that amylin depolarizes neurons while decreasing conductance. Amylin also resulted in a reduction in whole cell currents, consistent with the decrease in conductance. Amylin is also found to increase excitability of neurons. In the presence of TTX, spontaneous currents in cardiac preganglionic vagal neurons were observed to decrease in frequency in response to amylin while amplitude remained constant, signifying that amylin reduces presynaptic activity at cardiac preganglionic vagal neurons. Finally, evoked synaptic currents revealed that amylin decreases evoked currents, further demonstrating that amylin depolarization and increase in excitability of cardiac preganglionic vagal neurons is also associated with simultaneous inhibition of presynaptic transmission. Our study has demonstrated for the first time that the bradycardia elicited by the diving reflex is mediated by amylin from trigeminal ganglion cells projecting to cardiac preganglionic neurons in the nucleus ambiguus. Additionally, amylin results in the depolarization and increased excitability of cardiac preganglionic vagal neurons while inhibiting presynaptic transmission.

      Ling, Haibin; Shi, Justin Y.; Ji, Bo; Wu, Ziyan; Ling, Haibin (Temple University. Libraries, 2020)
      Object perception as a fundamental task in computer vision has a broad of applicationin real word, such as self-driving, industrial defect inspection, intelligent agriculture. Numerous works have been studied to advance the progress of object perception. In particular, due to the powerful feature learning and representation ability of deep learning, object perception algorithm has achieved signicant progress. In the dissertation, I rst introduce the denition of object perception and its three subtasks: object detection, pose estimation, and object segmentation; then specically present our works in the three subtasks, respectively. Object detection: we conduct two works, clustered object detection in aerial image (ClusDet) and dually supervised feature pyramid for object detection and segmentation (DSFPN). The ClusDet is designed to leverage the prior that objects in aerial ( especially in trac scenario) tend to cluster in dierent scales for object detection. By comparing with evenly crop method, ClusDet can achieve superior precision with less computation load. The DSFPN is proposed to alleviate the gradient degradation or vanishing problem in feature pyramid network (FPN) for object detection and segmentation. In particular, we note that performance of the two-stage detectors do not constantly increase with the growing complexity of backbone network, which is consistent with the conclusion in \deep residual learning for image recognition". To mitigate the problem, we propose to add extra supervision signal on bottom-up path of FPN in training phase to enhance the gradient information so as to facilitate the model training. Pose estimation: a robust dynamic fusion (RDF) algorithm is proposed to deal with noisy modalities in patient body modeling. In particular, for patient body modeling, the RGB camera cannot provide sucient information because of the body covered with blanket or loosen cloth. In this case, multi-modality (e.g., RGB, thermal, depth) sensors are required to acquire complimentary information. However, dierent application may need dierent sensors. It is labor-intensive and time-consuming to train a model per an application. In addition, multi-modality images may come to various noise in deployment, so that the trained model fails to work precisely. To deal with the aforementioned issues, we propose the RDF in conjunction with a dynamic training strategy to adaptively depress the features from noisy modalities, such that the model can be trained once and deployed any of the modalities. Object segmentation: the object here refers to crack, we propose a feature pyramid and hierarchical boosting network (FPHBN) for pavement crack detection. Specically, the crack in pavement has various scales (width), based on this characteristic, we introduce a feature pyramid architecture to utilize the inherent hierarchy of deep convolution networks (DConvNets) to construct multi-scale features for multi-scale cracks. Beside, each layer of the DConvNets is not independent, to leverage this dependency, we design a hierarchical boosting module to reweight samples via the prediction from adjunct layer. With the benet of the boosting module, the proposed network can dynamically pay more attention to hard samples.