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Abstract Universal chemotherapy in glioblastoma patients causes chemoresistance and further limits immune cells by creating an immunosuppressive tumor microenvironment that are difficult to solve by single‐drug therapeutic approaches. Here, this work designs hybrid drug‐loaded nanoliposomes by co‐loading the chemotherapeutic drug temozolomide (TMZ) and nitric oxide (NO) prodrug JS‐K with sphingosine‐1‐phosphate molecules (S1P) on the surface. The S1P‐S1P receptors axis endows nanoliposomes with rapid targeting and lysosomal escaping capability. Then, fine‐tuned TMZ release and NO gas production following JS‐K release in glioma microenvironment decrease chemoresistance and increase tumor immunogenicity through inhibiting the cellular autophagy as well as inducing mitochondrial dysfunction. RNA sequencing analysis demonstrates that the NO gas generation reprograms glioma microenvironment immune and inflammation‐related pathways. The positive immune response in turn effectively activates the enhanced efficacy of chemotherapy. NO gas generated nanoliposomes thus have attractive paradigm‐shifting applications in the treatment of “cold” tumors across a range of immunosuppressive indications.

One key issue in designing battery-powered wireless sensor networks is to properly control the energy consumption of the sensor nodes in order to prolong their operation time (i.e. lifetime ). In this article, we present a real-life application of wireless sensor networks in trains to monitor the goods conditions in a long-distance transportation. We study the wireless sensor network deployment problem in developing a monitoring system with the goal of maximizing the network lifetime under constraints derived from the real application scenario. The key technical problem to solve is to determine the sensor placement and the transmission level for each sensor node, as well as the appropriate number of sensor nodes. We first formulate the problem with a realistic discrete power model as a mixed integer linear programming problem. Then, a two-step efficient deployment heuristic is proposed to satisfy these constraints step by step. The evaluation results indicate that the proposed heuristic performs almost the same as the optimal mixed integer linear programming solution. Moreover, the wireless sensor network with appropriate number of nodes can improve its lifetime up to 10.6% for a train with 80 boxcars. We also discussed a tested experiment in a laboratory environment, as well as the real implementation of the whole monitoring system.

Recently, energy harvesting has been emerging as a promising technique to prolong the lifetime for wireless sensor nodes. Most existing efforts address the design of energy harvesting and sensor node subsystem separately or ignore some real-world constraints. In this paper, we study how to codesign the two subsystems and how to jointly manage energy harvesting, storage, and usage. We first propose a novel system architecture for energy harvesting which employs several supercapacitors to eliminate the conflicts on charging and discharging among different system components. Then, we present a method to schedule their charging and discharging, which is proved to be able to guarantee zero waste of the harvested energy if the battery is not full. Third, we propose an optimal algorithm to minimize different components’ capacity and two heuristic algorithms to maximize the system reward. We conduct extensive experiments based on real-life data traces. Results show that the proposed system architecture can harvest more energy compared to the state of the art, and the capacity optimization algorithm can choose the most suitable size for each system component.