A noninvasive and selective therapy, photodynamic therapy (PDT) is widely researched in clinical fields; however, the lower efficiency of PDT can induce unexpected side effects. Mitochondria are extensively researched as target sites to maximize PDT effects because they play crucial roles in metabolism and can be used as cancer markers due to their high transmembrane potential. Here, a mitochondria targeting photodynamic therapeutic agent (MitDt) is developed. This photosensitizer is synthesized from heptamethine cyanine dyes, which are conjugated or modified as follows. The heptamethine meso‐position is conjugated with a triphenylphosphonium derivative for mitochondrial targeting, the N‐alkyl side chain is modified for regulation of charge balance and solubility, and the indolenine groups are brominated to enhance reactive oxygen species generation (ROS) after laser irradiation. The synthesized MitDt increases the cancer uptake efficiency due to the lipo‐cationic properties of the triphenylphosphonium, and the PDT effects of MitDt are amplified after laser irradiation because mitochondria are susceptible to ROS, the response to which triggers an apoptotic anticancer effect. Consequently, these hypotheses are demonstrated by in vitro and in vivo studies, and the results indicate strong potential for use of MitDts as efficient single‐molecule‐based PDT agents for cancer treatment.

In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with “moving noises”, which appear every night in a commercial pig farm, but have not been reported yet. We first apply a spatiotemporal interpolation technique to remove the moving noises occurring in the depth images. Then, we detect the standing-pigs by utilizing the undefined depth values around them. Our experimental results show that this method is effective for detecting standing-pigs at night, in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (i.e., 94.47%), even with severe moving noises occluding up to half of an input depth image. Furthermore, without any time-consuming technique, the proposed method can be executed in real-time.

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