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Hybrid organic-inorganic perovskites (HOIPs) are promising candidates for next-generation photovoltaic materials. However, there is a debate regarding the impact of interactions between the organic center and the surrounding inorganic cage on the solar cell's high diffusion lengths. It remains unclear whether the diffusion mechanism is consistent across various halide perovskite families and how light illumination affects carrier lifetimes. The focus is on ion kinetics of (CH3NH3)PbX3 (X = Br, Cl) perovskite halide single crystals. Muon spectroscopy (mu+SR)is employed to investigate the fluctuations and diffusion of ions via the relaxation of muon spins in local nuclear field environments. Within a temperature range of 30-340 K, ion kinetics are studied with and without white-light illumination. The results show a temperature shift of the tetragonal-orthorhombic phase transition on the illuminated samples, as an effect of increased organic molecule fluctuations. This relation is supported by density functional theory (DFT) calculations along the reduction of the nuclear field distribution width between the phase transitions. The analysis shows that, depending on the halide ion, the motional narrowing from H and N nuclear moments represents the molecular fluctuations. The results demonstrate the importance of the halide ion and the effect of illumination on the compound's structural stability and electronic properties.

Surveillance videos of operating rooms have potential to benefit post-operative analysis and study. However, there is currently no effective method to extract useful information from the long and massive videos. As a step towards tackling this issue, we propose a novel method to recognize and evaluate individual activities using an anomaly estimation model based on time-sequential prediction. We verified the effectiveness of our method by comparing two time-sequential features: individual bounding boxes and body key points. Experiment results using actual surgery videos show that the bounding boxes are suitable for predicting and detecting regional movements, while the anomaly scores using key points can hardly be used to detect activities. As future work, we will be proceeding with extending our activity prediction for detecting unexpected and urgent events.

In vertebrates, newly emerging transformed cells are often apically extruded from epithelial layers through cell competition with surrounding normal epithelial cells. However, the underlying molecular mechanism remains elusive. Here, using phospho-SILAC screening, we show that phosphorylation of AHNAK2 is elevated in normal cells neighboring RasV12 cells soon after the induction of RasV12 expression, which is mediated by calcium-dependent protein kinase C. In addition, transient upsurges of intracellular calcium, which we call calcium sparks, frequently occur in normal cells neighboring RasV12 cells, which are mediated by mechanosensitive calcium channel TRPC1 upon membrane stretching. Calcium sparks then enhance cell movements of both normal and RasV12 cells through phosphorylation of AHNAK2 and promote apical extrusion. Moreover, comparable calcium sparks positively regulate apical extrusion of RasV12-transformed cells in zebrafish larvae as well. Hence, calcium sparks play a crucial role in the elimination of transformed cells at the early phase of cell competition.

Appropriate evaluation of the intraoperative state of a surgical team is essential for the improvement of teamwork and hence a safe surgical environment. Traditional methods to evaluate intraoperative team states such as interview and self-check questionnaire on each surgical team member often require human efforts, which are time-consuming and can be biased by individual recall. One effective solution is to analyze the surgical video and track the important team activities, such as whether the members are complying with the surgical procedure or are being distracted by unexpected events. However, due to the complexity of the situations in an operating room, identifying the team activities without any human effort remains challenging. In this work, we propose a novel approach that automatically recognizes and quantifies intraoperative activities from surgery videos. As a first step, we focus on recognizing two activities that especially involve multiple individuals: (a) passing of clean-packaged surgery instruments which is a representative interaction between the surgical technologists such as the circulating nurse and scrub nurse, and (b) group attention that may be attracted by unexpected events. We record surgical videos as input, and apply pose estimation and particle filters to extract individual's face orientation, body orientation, and arm raise. These results coupled with individual IDs are then sent to an estimation model that provides the probability of each target activity. Simultaneously, a person model is generated and bound to each individual, which describes all the involved activities along the timeline. We tested our method using videos of simulated activities. The results showed that the system was able to recognize instrument passing and group attention with F1 = 0.95 and F1 = 0.66, respectively. We also implemented a system with an interface that automatically annotated intraoperative activities along the video timeline, and invited feedback fro