In this study, we describe a new strategy for labeling and tracking lysosomes with a cell-permeable fluorescent activity-based probe (CpFABP) that is covalently bound to select lysosomal proteins. Colocalization studies that utilized LysoTracker probes as standard lysosomal markers demonstrated that our novel probe is effective in specifically labeling lysosomes in various kinds of live cells. Furthermore, our studies revealed that this probe has the ability to label fixed cells, permeabilized cells, and NH4Cl-treated cells, unlike LysoTracker probes, which show ineffective labeling under the same conditions. Remarkably, when applied to monitor the process of lysosome-dependent apoptosis, our probe not only displayed the expected release of lysosomal cathepsins from lysosomes into the cytosol but also revealed additional information about the location of the cathepsins during apoptosis, which is undetectable by other chemical lysosome markers. These results suggest a wide array of promising applications for our probe and provide useful guidelines for its use as a lysosome marker in lysosome-related studies.
Mobile Edge Computing (MEC) has been widely employed to support various Internet of Things (IoT) and mobile applications. By leveraging the advantages of easily deployed and flexibility of Unmanned Aerial Vehicle (UAV), one of MEC primary functions is employing UAVs equipped with MEC servers to provide computation supports for the offloaded tasks by mobile users in temporally hotspot areas or some emergent scenarios, such as sports game areas or destroyed by natural disaster areas. Despite the numerous advantages of UAV carried with a MEC server, it is restricted by its limited computation resources and sensitive energy consumption. Moreover, due to the complexity of UAV-assisted MEC system, its computational resource optimization and energy consumption optimization cannot be achieved well in traditional optimization methods. Furthermore, the computational cost of the MEC system optimization is often exponentially growing with the increase of the MEC servers and mobile users. Therefore, it is considerably challenging to control the UAV positions and schedule the task offloading ratio. In this paper, we proposed a novel Deep Reinforcement Learning (DRL) method to optimize UAV trajectory controlling and users' offloaded task ratio scheduling and improve the performance of the UAV-assisted MEC system. We maximized the system stability and minimized the energy consumption and computation latency of UAV-assisted the MEC system. The simulation results show that the proposed method outperforms existing work and has better scalability.INDEX TERMS Computation offloading, deep learning, energy saving, mobile edge computing, reinforcement learning, unmanned aerial vehicle.
In vivo optical spatio-temporal imaging of the tumor microenvironment is useful to explain how tumor immunotherapies work. However, the lack of fluorescent antigens with strong immunogenicity makes it difficult to study the dynamics of how tumors are eliminated by any given immune response. Here, we develop an effective fluorescent model antigen based on the tetrameric far-red fluorescent protein KatushkaS158A (tfRFP), which elicits both humoral and cellular immunity. We use this fluorescent antigen to visualize the dynamic behavior of immunocytes as they attack and selectively eliminate tfRFP-expressing tumors in vivo; swarms of immunocytes rush toward tumors with high motility, clusters of immunocytes form quickly, and numerous antigen-antibody complexes in the form of tfRFP+ microparticles are generated in the tumor areas and ingested by macrophages in the tumor microenvironment. Therefore, tfRFP, as both a model antigen and fluorescent reporter, is a useful tool to visualize specific immune responses in vivo.
Abstract-In multi-winding transformers, different geometry of the individual windings leads to unbalanced leakage flux paths. The unbalance affects the behaviour of the power electronic converters, where these transformers are used. This work proposes an approach to model the leakage flux path of transformer with repetitive multi-winding structure using permeance magnetic circuit. The model is composed of lumped components, it can be seamlessly integrated into system-level simulation of power electronic circuits and achieve good accuracy in time-domain simulation. Taking advantage of the repetitive structure, the model requires very limited number of parameters, which can be easily obtained from the geometry information together with only a few experimental tests. The fidelity of the model is experimentally confirmed on a multi-winding transformer prototype connected to power electronic devices.
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