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Pest control is crucial in crop production; however, the use of chemical pesticides, the primary method of pest control, poses environmental issues and leads to insecticide resistance in pests. To overcome these issues, laser zapping has been studied as a clean pest control technology against the nocturnal cotton leafworm, Spodoptera litura, which has high fecundity and causes severe damage to various crops. For better sighting during laser zapping, it is important to measure the coordinates and speed of moths under low-light conditions. To achieve this, we developed an automatic detection pipeline based on point cloud time series data from stereoscopic images. We obtained 3D point cloud data from disparity images recorded under infrared and low-light conditions. To identify S. litura, we removed noise from the data using multiple filters and a support vector machine. We then computed the size of the outline box and directional angle of the 3D point cloud time series to determine the noisy point clouds. We visually inspected the flight trajectories and found that the size of the outline box and the movement direction were good indicators of noisy data. After removing noisy data, we obtained 68 flight trajectories, and the average flight speed of free-flying S. litura was 1.81 m/s.

Homeobox protein B13 (HOXB13), a transcription factor, is related to methylmercury toxicity; however, the downstream factors involved in enhancing methylmercury toxicity remain unknown. We performed microarray analysis to search for downstream factors whose expression is induced by methylmercury via HOXB13 in human embryonic kidney cells (HEK293), which are useful model cells for analyzing molecular mechanisms. Methylmercury induced the expression of oncostatin M (OSM), a cytokine of the interleukin-6 family, and this was markedly suppressed by HOXB13 knockdown. OSM knockdown also conferred resistance to methylmercury in HEK293 cells, and no added methylmercury resistance was observed when both HOXB13 and OSM were knocked down. Binding of HOXB13 to the OSM gene promoter was increased by methylmercury, indicating the involvement of HOXB13 in the enhancement of its toxicity. Because addition of recombinant OSM to the medium enhanced methylmercury toxicity in OSM-knockdown cells, extracellularly released OSM was believed to enhance methylmercury toxicity via membrane receptors. We discovered tumor necrosis factor receptor (TNF) receptor 3 (TNFR3) to be a potential candidate involved in the enhancement of methylmercury toxicity by OSM. This toxicity mechanism was also confirmed in mouse neuronal stem cells. We report, for the first time, that HOXB13 is involved in enhancement of methylmercury toxicity via OSM-expression induction and that the synthesized OSM causes cell death by binding to TNFR3 extracellularly.

The Industrial Internet of Things (IIoT) applications are required to provide precise measurement functions as feedback for controlling devices. The applications traditionally use polling-based communication protocols. However, in polling-based communication over current industrial wireless network protocols such as ISA100.11a, WirelessHART have difficulty in realizing both scheduled periodic data collection at high success ratio and unpredictable on-demand communications with short latency. In this paper, a polling-based transmission scheme using a network traffic uniformity metric is proposed for IIoT applications. In the proposed scheme, a center node controls the transmission timing of all polling-based communication in accordance with a schedule that is determined by a Genetic Algorithm. Communication of both periodic and unpredictable on-demand data collection are uniformly assigned to solve the above difficulties in the schedule. Simulation results show that network traffic is generated uniformly and a center node can collect periodic data from nodes at high success ratio. The average success probability of periodical data collection is 97.4 % and the lowest probability is 95.2 % .