Aims: The NADPH oxidase (NOX) family of enzymes catalyzes the formation of reactive oxygen species (ROS). NOX enzymes not only have a key role in a variety of physiological processes but also contribute to oxidative stress in certain disease states. To date, while numerous small molecule inhibitors have been reported (in particular for NOX2), none have demonstrated inhibitory activity in vivo. As such, there is a need for the identification of improved NOX inhibitors to enable further evaluation of the biological functions of NOX enzymes in vivo as well as the therapeutic potential of NOX inhibition. In this study, both the in vitro and in vivo pharmacological profiles of GSK2795039, a novel NOX2 inhibitor, were characterized in comparison with other published NOX inhibitors. Results: GSK2795039 inhibited both the formation of ROS and the utilization of the enzyme substrates, NADPH and oxygen, in a variety of semirecombinant cell-free and cellbased NOX2 assays. It inhibited NOX2 in an NADPH competitive manner and was selective over other NOX isoforms, xanthine oxidase, and endothelial nitric oxide synthase enzymes. Following systemic administration in mice, GSK2795039 abolished the production of ROS by activated NOX2 enzyme in a paw inflammation model. Furthermore, GSK2795039 showed activity in a murine model of acute pancreatitis, reducing the levels of serum amylase triggered by systemic injection of cerulein. Innovation and Conclusions: GSK2795039 is a novel NOX2 inhibitor that is the first small molecule to demonstrate inhibition of the NOX2 enzyme in vivo. Antioxid. Redox Signal. 23, 358-374.

Pedestrian detection and classification are of increased interest in the intelligent transportation system (ITS), and among the challenging issues, we can find limitations of tiny and occluded appearances, large variation of human pose, cluttered background, and complex environment. In fact, a partial occlusion handling is important in the case of detecting pedestrians, in order to avoid accidents between pedestrians and vehicles, since it is difficult to detect when pedestrians are suddenly crossing the road. To solve the partial occlusion problem, a pyramidal part-based model (PPM) is proposed to obtain a more accurate prediction based on the majority vote of the confidence score of the visible parts by cascading the pyramidal structure. The experimental results on the proposed scheme achieved 96.25% accuracy on the INRIA dataset and 81% accuracy on the PSU (Prince of Songkla University) dataset. Our proposed model can be applied in the real-world environment to classify the occluded part of pedestrians with the various information of part representation at each pyramid layer.

<span lang="EN-US">The investigation of a deep neural network for pedestrian classification using transfer learning methods is proposed in this study. The development of deep convolutional neural networks has significantly improved the autonomous driver assistance system for pedestrian classification. However, the presence of partially occluded parts and the appearance variation under complex scenes are still robust to challenge in the pedestrian detection system. To address this problem, we proposed six transfer learning models: end-to-end convolutional neural network (CNN) model, scratch-trained residual network (ResNet50) model, and four transfer learning models: visual geometry group 16 (VGG16), GoogLeNet (InceptionV3), ResNet50, and MobileNet. The performance of the pedestrian classification was evaluated using four publicly datasets: </span><em><span lang="EN-US">Institut National de Recherche en Sciences et Technologies du Numérique</span></em><span lang="EN-US"> (INRIA), Prince of Songkla University (PSU), CVC05, and Walailak University (WU) datasets. The experimental results show that six transfer learning models achieve classification accuracy of 65.2% (end-to-end CNN), 92.92% (scratch-trained ResNet50), 97.15% (pre-trained VGG16), 94.39% (pre-trained InceptionV3), 90.43% (pre-trained ResNet50), and 98.69% (pre-trained MobileNet) using data from Southern Thailand (PSU dataset). Further analysis reveals that the deeper the ConvNet architecture, the more specific information of features is provided. In addition, the deep ConvNet architecture can distinguish pedestrian occluded patterns while being trained with partially occluded parts of data samples.</span>

Pedestrian classification is of increased interest to autonomous transportation systems due to the development of deep convolutional neural networks. Despite recent progress on pedestrian classification, it is still challenging to identify individuals who are partially occluded because of the diversity of the occluded parts, variation in pose, and appearance. This causes a significant performance reduction when pedestrians are covered by other objects, and feature information is lost due to the occluded parts. To solve this problem, we propose two network architectures using tree structure convolutional neural networks (T-CNN). They use the structural representation of multi-branch deep convolutional features, with the advantages of its end-to-end learning process. The high-level tree structure CNN (HT-CNN) architecture aims to concatenate the output of the classification layer from multi-segmented patches of pedestrians to handle partially occluded problems. The low-level tree structure CNN (LT-CNN) concatenates the discriminative features from each multi-segmented patch and global features. Our T-CNN architecture with a high-level tree structure performed with 94.64% accuracy on the INRIA dataset without occlusions, and with 70.78% accuracy on the Prince of Songkla University (PSU) dataset with occlusions, outperforming a baseline CNN architecture. This indicates that our proposed architecture can be used in a real-world environment to classify the occluded part of pedestrians with the visual information of multi-segmented patches using tree-structured multi-branched CNN.

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