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Gastric cancer is a heterogeneous group of diseases with only a fraction of patients responding to immunotherapy. The relationships between tumor DNA damage response, patient immune system and immunotherapy have recently attracted attention. Accumulating evidence suggests that DNA repair landscape is a significant factor in driving response to immune checkpoint blockade (ICB) therapy. In this study, to explore new prognostic and predictive biomarkers for gastric cancer patients who are sensitive and responsive to immunotherapies, we developed a novel 15-DNA repair gene signature (DRGS) and its related scoring system and evaluated the efficiency of the DRGS in discriminating different molecular and immune characteristics and therapeutic outcomes of patients with gastric adenocarcinoma, using publicly available datasets. The results demonstrated that DRGS high score patients showed significantly better therapeutic outcomes for ICB compared to DRGS low score patients (p < 0.001). Integrated analysis of multi-omics data demonstrated that the patients with high DRGS score were characteristic of high levels of anti-tumor lymphocyte infiltration, tumor mutation burden (TMB) and PD-L1 expression, and these patients exhibited a longer overall survival, as compared to the low-score patients. Results obtained from HPA and IHC supported significant dysregulation of the genes in DRGS in gastric cancer tissues, and a positive correlation in protein expression between DRGS and PD-L1. Therefore, the DRGS scoring system may have implications in tailoring immunotherapy in gastric cancers. A preprint has previously been published (Yuan et al., 2021).

Silicon oxide (SiOx, 0 < x ≤ 2) has been recognized as a prominent anode material in lithium‐ion batteries and sodium‐ion batteries due to its high theoretical capacity, suitable electrochemical potential, and earth abundance. However, it is intrinsically poor electronic conductivity and excessive volume expansion during potassiation/depotassiation process hinder its application in potassium‐ion batteries. Herein, we reported a hierarchical porous C/SiOx potassium‐ion batteries anode using lignite as raw material via a one‐step carbonization and activation method. The amorphous C skeleton around SiOx particles can effectively buffer the volume expansion, and improve the ionic/electronic conductivity and structural integrity, achieving outstanding rate capability and cyclability. As expected, the obtained C/SiOx composite delivers a superb specific capacity of 370 mAh g−1 at 0.1 A g−1 after 100 cycles as well as a highly reversible capacity of 208 mAh g−1 after 1200 cycles at 1.0 A g−1. Moreover, the potassium ion storage mechanism of C/SiOx electrodes was investigated by ex‐situ X‐ray diffraction and transmission electron microscopy, revealing the formation of reversible products of K6.8Si45.3 and K4SiO4, accompanied by generation of irreversible K2O after the first cycle. This work sheds light on designing low‐cost Si‐based anode materials for high‐performance potassium‐ion batteries and beyond. [ABSTRACT FROM AUTHOR]

As the next generation of green power system, smart grids have gradually enhanced the operation efficiency of power system. Meanwhile, the application of communication and intelligent technologies make the power grid more vulnerable to the emerging cyber‐physical attacks, such as the false data injection attack (FDIA). Particularly, the deception property of the FDIA on the output measurement estimation can fool the current security mechanism without triggering an alarm. Motivated by this problem, this paper aims at developing a novel detection and recovery mechanism against FDIA in smart grid. Based on the established state space grid model derived from the three‐phase sinusoidal voltage equations, an improved principal component analysis (PCA)‐based detection method is proposed. By introducing the mathematical transformation principle method, the detection performance such as detection rate and false positive rate can be improved. To keep the stable running of power system, a genetic optimization algorithm‐based linear quadratic regulator (LQR) defense method is developed. In addition, to improve the response performance to external attacks, an artificial intelligence method named genetic optimization algorithm is introduced to optimize the robust performance of the proposed defense method. Finally, the simulation results on the IEEE 6‐bus and 118‐bus grid system demonstrate the superiority of the proposed genetic algorithm optimization‐based LQR defense method. [ABSTRACT FROM AUTHOR]