Coal fly dust (CFD)-induced asthma model is used as an ambient particulate matter model of serious pulmonary damage. We aimed to evaluate the effects of a combination of ginseng and Salvia plebeia R. Br extract (KGC-03-PS; KG3P) and its individual components (hispidulin, nepetin and rosmarinic acid) in a CFD-induced mouse model of airway inflammation (asthma). We also evaluated signal transduction by KG3P and its individual components in the alveolar macrophage cell line, MH-S cells. In vitro, KG3P and its individual components inhibited nitric oxide production and expression of pro-inflammatory mediators and cytokines (iNOS, COX-2, IL-1β, IL-6 and TNF-α) through the NF-κB and MAPK pathways in coal fly ash (CFA)-induced inflammation in MH-S cells. Moreover, in the CFD-induced asthma model in mice, KG3P and its predominant individual component, nepetin, inhibited Asymmetric Dimethyl arginine (ADMA) and Symmetric Dimethyl arginine (SDMA) in serum, and decreased the histopathologic score in the lungs. A significant reduction in the neutrophils and immune cells in BALF and lung tissue was demonstrated, with significant reduction in the expression of the pro-inflammatory cytokines. Finally, IRAK-1 localization was also potently inhibited by KG3P and nepetin. Thus, KG3P extract can be considered as a potent candidate for amelioration of airway inflammation.
This research was a part of the project titled 'Marine digital AtoN information management and service system development(1/5) (20210650)', funded by the Ministry of Oceans and Fisheries, Korea.."ABSTRACT This paper presents drone classification at millimeter-wave (mmWave) radars using the deep learning (DL) technique. The adoption mmWave technology in radar systems enables better resolution and aid in detecting smaller drones. Using radar cross-section (RCS) signature enables us to detect malicious drones and suitable action can be taken by respective authorities. Existing drone classification converts the RCS signature into images and then performs drone classification using a convolution neural network (CNN).Converting every signature into an image induces additional computation overhead; further CNN model is trained considering fixed learning rate. Thus, when using CNN-based drone classification under a highly dynamic environment exhibit poor classification accuracy. This paper present an im-proved long short-term memory (LSTM) by introducing a weight optimization model that can reduce computation overhead by not allowing the gradient to not flow through hidden states of the LSTM model. Further, present adaptive learning rate optimizing (ALRO) model for training the LSTM model. Experiment outcome shows LSTM-ALRO achieves much better drone detection accuracies when compared with the existing CNN-based drone classification model.
INDEX TERMSConvolutional neural network, drone detection, micro doppler signature (MDS), millimeter-wave, radar cross-section, unmanned aerial vehicle.
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