Both production of DNA damage and subsequent prevention of its repair are crucial in concluding the therapeutic outcome of radiotherapy (RT). However, nearly all current strategies for improving RT focus only on one of the two aspects and overlook the necessity of their combinations. In this work, we introduce a concept of DNAdual-targeting nanomedicine (NM) to simultaneously enhance DNA lesion formation and prevent the succeeding repair. Briefly, the cisplatin prodrug loaded in NM can form platinated DNA in cell nuclei, making DNA more vulnerable to the ionizing radiation generated by RT. Concomitantly, the spatial-temporally codelivered vorinostat, a histone deacetylase inhibitor, prolongs the build-up of double-strand breaks and causes cell apoptosis en masse, probably due to the suppressed expression of DNA repair proteins. Furthermore, this nanoplatform is suitable for fluorescence and magnetic resonance imaging techniques, enabling accurate trafficking of the NM as well as reliable real-time imaging-guided precision RT. Finally, results from in vitro and in vivo jointly reveal that this dualaction system attains a remarkably enhanced radiotherapeutic outcome. In conclusion, our imaging-guided DNA-dual-targeting design represents a novel strategy for efficient cancer precision RT.
Security-constrained economic dispatch (SCED) is one of the most important daily tasks for operators. The scale of security constraints is huge for practically-sized power systems, which makes the SCED difficult or even impossible to be solved. Whereby, the number of active security constraints is relatively small. By eliminating the inactive security constraints, the complexity of SCED can be significantly reduced. Focusing on it, this paper proposes an intelligent framework to accelerate the calculation of SCED without any loss of accuracy. The proposed framework uses a deep neural network (DNN) to reduce the online computational cost significantly by shifting the heavy computation into offline training. More specifically, a DNN is used to extract the feature of SCED, which can effectively pre-identify the active constraints of SCED model. Moreover, an efficient lightweight learning strategy is presented to improve the learning efficiency of the DNN by the feature selection and feature decomposition. The effectiveness of the proposed method is demonstrated in modified IEEE benchmark systems.
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