The UCSC database was used to predict that acetylation of histone H3 at lysine 27 (H3K27ac) induces binding to the PSTPIP2 promoter, and this prediction was validated by a ChIP assay. Interestingly, an HDAC-specific inhibitor (TSA) was sufficient to potently upregulate PSTPIP2 in epithelial cells. Histone acetylation-mediated silencing of PSTPIP2 may contribute to cisplatin nephrotoxicity. PSTPIP2 may serve as a potential therapeutic target in the prevention of cisplatin nephrotoxicity.Reversing the highly immunosuppressive tumor microenvironment (TME) is essential to achieve long-term efficacy with cancer immunotherapy. Despite the impressive clinical response to checkpoint blockade in multiple types of cancer, only a minority of patients benefit from this approach. Here, we report that the oncolytic virus M1 induces immunogenic tumor cell death and subsequently restores the ability of dendritic cells to prime antitumor T cells. Intravenous injection of M1 disrupts immune tolerance in the privileged TME, reprogramming immune-silent (cold) tumors into immune-inflamed (hot) tumors. find more M1 elicits potent CD8+ T cell-dependent therapeutic effects and establishes long-term antitumor immune memory in poorly immunogenic tumor models. Pretreatment with M1 sensitizes refractory tumors to subsequent checkpoint blockade by boosting T-cell recruitment and upregulating the expression of PD-L1. These findings reveal the antitumor immunological mechanism of the M1 virus and indicated that oncolytic viruses are ideal cotreatments for checkpoint blockade immunotherapy.Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.Cervical cancer is one of the most common gynecological tumors in the world, and human papillomavirus (HPV) infection is its causative agent. However, the molecular mechanisms involved in the carcinogenesis of cervical cancer still require clarification. Here we found that knockdown of Non-SMC (Structural Maintenance of Chromosomes) condensin I complex subunit H (NCAPH) gene expression significantly inhibited the proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) of cervical cancer cells in vitro, and restrained xenograft tumor formation in vivo. Intriguingly, HPV E7 could form a positive feedback loop with NCAPH. E7 upregulated NCAPH gene expression via E2F1 which initiated NCAPH transcription by binding to its promoter directly. Silencing of NCAPH reduced E7 transcription via promoting the transition of AP-1 heterodimer from c-Fos/c-Jun to Fra-1/c-Jun. Moreover, the E7-mediated NCAPH overexpression was involved in the activation of the PI3K/AKT/SGK signaling pathway. In vivo, NCAPH expression in cervical cancer tissues was significantly higher than which in normal cervix and high-grade squamous intraepithelial lesion (HSIL) tissues, and its expression was significantly correlated with tumor size, depth of invasion and lymph node metastasis. Patients with high NCAPH expression had a significantly better survival outcomes than those with low-expression, suggesting that NCAPH-induced cell proliferation might sensitize cancer cells to adjuvant therapy. In conclusion, our results revealed the role of NCAPH in the carcinogenesis of cervical cancer in vitro and in vivo. The interaction between E7 and NCAPH expands the mechanism of HPV induced tumorigenesis and that of host genes regulating HPV E7.The formation of zygote is the beginning of mammalian life, and dynamic epigenetic modifications are essential for mammalian normal development. H3K27 di-methylation (H3K27me2) and H3K27 tri-methylation (H3K27me3) are marks of facultative heterochromatin which maintains transcriptional repression established during early development in many eukaryotes. However, the mechanism underlying establishment and regulation of epigenetic asymmetry in the zygote remains obscure. Here we show that maternal EZH2 is required for the establishment of H3K27me3 in mouse zygotes. However, combined immunostaining with ULI-NChIP-seq (ultra-low-input micrococcal nuclease-based native ChIP-seq) shows that EZH1 could partially safeguard the role of EZH2 in the formation of H3K27me2. Meanwhile, we identify that EHMT1 is involved in the establishment of H3K27me2, and that H3K27me2 might be an essential prerequisite for the following de novo H3K27me3 modification on the male pronucleus. In this work, we clarify the establishment and regulatory mechanisms of H3K27me2 and H3K27me3 in mouse zygotes.RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and the number of features is high, which is a common situation in biomedical settings. To address these limitations, we propose a meta-learning framework based on neural networks for survival analysis and evaluate it in a genomic cancer research setting. We demonstrate that, compared to regular transfer-learning, meta-learning is a significantly more effective paradigm to leverage high-dimensional data that is relevant but not directly related to the problem of interest. Specifically, meta-learning explicitly constructs a model, from abundant data of relevant tasks, to learn a new task with few samples effectively. For the application of predicting cancer survival outcome, we also show that the meta-learning framework with a few samples is able to achieve competitive performance with learning from scratch with a significantly larger number of samples.