The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

Overactivation of microglial cells may cause severe brain tissue damage in various neurodegenerative diseases. Therefore, the overactivation of microglia should be repressed by any means. The present study investigated the potential mechanism and signaling pathway for the repressive effect of TGF-β1, a major anti-inflammatory cytokine, on overactivation and resultant death of microglial cells. A bacterial endotoxin LPS stimulated expression of inducible NO synthase (iNOS) and caused death in cultured microglial cells. TGF-β1 markedly blocked these LPS effects. However, the LPS-evoked death of microglial cells was not solely attributed to excess production of NO. Because phosphatidylinositol 3-kinase (PI3K) was previously shown to play a crucial role in iNOS expression and cell survival signals, we further studied whether PI3K signaling was associated with the suppressive effect of TGF-β1. Like TGF-β1, the PI3K inhibitor LY294002 blocked iNOS expression and death in cultured microglial cells. Both TGF-β1 and LY294002 decreased the activation of caspases 3 and 11 and the mRNA expression of various kinds of inflammatory molecules caused by LPS. TGF-β1 was further found to decrease LPS-induced activation of PI3K and Akt. TGF-β1 and LY294002 suppressed LPS-induced p38 mitogen-activated kinase and c-Jun N-terminal kinase activity. In contrast, TGF-β1 and LY294002 enhanced LPS-induced NF-κB activity. Our data indicate that TGF-β1 protect normal or damaged brain tissue by repressing overactivation of microglial cells via inhibition of PI3K and its downstream signaling molecules.

Lymph nodes examined and margins obtained are important quality metrics in wedge resection. A high-quality wedge appears to confer a significant survival advantage over lower-quality wedge and stereotactic radiation. A margin-positive wedge appears to offer no benefit compared with radiation.

Unmanned aerial vehicles (UAVs), which are commonly known as drones, have proved to be useful not only on the battlefields where manned flight is considered too risky or difficult, but also in everyday life purposes such as surveillance, monitoring, rescue, unmanned cargo, aerial video, and photography. More advanced drones make use of global positioning system (GPS) receivers during the navigation and control loop which allows for smart GPS features of drone navigation. However, there are problems if the drones operate in heterogeneous areas with no GPS signal, so it is important to perform research into the development of UAVs with autonomous navigation and landing guidance using computer vision. In this research, we determined how to safely land a drone in the absence of GPS signals using our remote maker-based tracking algorithm based on the visible light camera sensor. The proposed method uses a unique marker designed as a tracking target during landing procedures. Experimental results show that our method significantly outperforms state-of-the-art object trackers in terms of both accuracy and processing time, and we perform test on an embedded system in various environments.