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The prenylated form of the human 2'-5'-oligoadenylate synthetase 1 (OAS1) protein has been shown to potently inhibit the replication of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the virus responsible for the Coronavirus Disease 2019 (COVID-19) pandemic. However, the OAS1 orthologue in the horseshoe bats (superfamily Rhinolophoidea), the reservoir host of SARS-related coronaviruses (SARSr-CoVs), has lost the prenylation signal required for this antiviral activity. Herein, we used an ancestral state reconstruction approach to predict and reconstitute in vitro, the most likely OAS1 protein sequence expressed by the Rhinolophoidea common ancestor prior to its prenylation loss (RhinoCA OAS1). We exogenously expressed the ancient bat protein in vitro to show that, unlike its non-prenylated horseshoe bat descendants, RhinoCA OAS1 successfully blocks SARS-CoV-2 replication. Using protein structure predictions in combination with evolutionary hypothesis testing methods, we highlight sites under unique diversifying selection specific to OAS1's evolution in the Rhinolophoidea. These sites are located near the RNA-binding region and the C-terminal end of the protein where the prenylation signal would have been. Our results confirm that OAS1 prenylation loss at the base of the Rhinolophoidea clade ablated the ability of OAS1 to restrict SARSr-CoV replication and that subsequent evolution of the gene in these bats likely favoured an alternative function. These findings can advance our understanding of the tightly linked association between SARSr-CoVs and horseshoe bats.

Power grid fault diagnosis methods based on analytical models possess advantages such as logical rigor, strong interpretability, and high applicability. However, the existing design of their objective functions is presented as a 0–1 integer nonlinear programming model, making it difficult to approximate the optimal solution. To address this issue, this paper proposes an improvement to the expression of the second backup protection expectation involved in the diagnostic model. Thus, the order of the objective function is successfully reduced, transforming the original 0–1 integer nonlinear programming model into a constrained 0–1 integer linear programming model, which can be efficiently solved by typical commercial solvers based on a linear integer programming framework. Numerical results demonstrate that the solution time of the proposed linearized model is lower than that of existing nonlinear programming models, and the diagnostic accuracy of the proposed model is higher than that of other state-of-the-art methods.

The symmetry of vibration signals collected from healthy machinery, which gradually degenerates with the development of faults, must be detected for timely diagnosis and prognosis. However, conventional methods may miss spatiotemporal relationships, struggle with varying sampling rates, and lack adaptability to changing loads and conditions, affecting diagnostic accuracy. A novel bearing fault diagnosis approach is proposed to address these issues, which integrates the Gramian angular field (GAF) transformation with a parallel deep convolutional neural network (DCNN). The crux of this method lies in the preprocessing of input signals, where sampling rate normalization is employed to minimize the effects of varying sampling rates on diagnostic outcomes. Subsequently, the processed signals undergo GAF transformation, converting them into an image format that effectively represents their spatiotemporal relationships in a two-dimensional space. These images serve as inputs to the parallel DCNN, facilitating feature extraction and fault classification through deep learning techniques and leading to improved generalization capabilities on test data. The proposed method achieves an overall accuracy of 96.96%, even in the absence of training data within the test set. Discussions are also conducted to quantify the effects of sampling rate normalization and model structures on diagnostic accuracy.

The resolving power of metalens telescopes rely on their aperture size. Flat telescopes are advancing with the research on super-resolution confocal metalenses with large aperture. However, the aperture sizes of metalenses are usually bound within hundreds of micrometers due to computational and fabrication challenges, limiting their usage on practical optical devices like telescopes. In this work, we demonstrated a two-step designing method for the design of dual-band far-field super-resolution metalens with aperture sizes from the micro-scale to macro-scale. By utilizing two types of inserted unit cells, the phase profile of a dual-wavelength metalens with a small aperture of 100 μm was constructed. Through numerical simulation, the measured FWHM values of the focal spots of 5.81 μm and 6.81 μm at working wavelengths of 632.8 nm and 1265.6 nm were found to all be slightly smaller than the values of 0.61 λ/NA, demonstrating the super-resolution imaging of the designed metalens. By measuring the optical power ratio of the focal plane and the incident plane, the focusing efficiencies were 76% at 632.8 nm and 64% at 1265.6 nm. Based on the design method for small-aperture metalens, far-field imaging properties through the macro metalens with an aperture of 40 mm were simulated by using the Huygens–Fresnel principle. The simulation results demonstrate confocal far-field imaging behavior at the target wavelengths of 632.8 nm and 1265.6 nm, with a focal length of 200 mm. The design method for dual-band far-field super-resolution metalens with a large aperture opens a door towards the practical applications in the dual-band space telescope system.

Abstract Background Protein arginine methyltransferases (PRMTs) regulate protein biological activity by modulating arginine methylation in cancer and are increasingly recognized as potential drug targets. Inhibitors targeting PRMTs are currently in the early phases of clinical trials and more candidate drugs are needed. Flavokawain A (FKA), extracted from kava plant, has been recognized as a potential chemotherapy drug in bladder cancer (BC), but its action mechanism remains unclear. Methods We first determined the role of a type II PRMT, PRMT5, in BC tissue samples and performed cytological experiments. We then utilized bioinformatics tools, including computational simulation, virtual screening, molecular docking, and energy analysis, to identify the potential use of PRMT5 inhibitors for BC treatment. In vitro and in vivo co-IP and mutation assays were performed to elucidate the molecular mechanism of PRMT5 inhibitor. Pharmacology experiments like bio-layer interferometry, CETSA, and pull-down assays were further used to provide direct evidence of the complex binding process. Results Among PRMTs, PRMT5 was identified as a therapeutic target for BC. PRMT5 expression in BC was correlated with poor prognosis and manipulating its expression could affect cancer cell growth. Through screening and extensive experimental validation, we recognized that a natural product, FKA, was a small new inhibitor molecule for PRMT5. We noticed that the product could inhibit the action of BC, in vitro and in vivo, by inhibiting PRMT5. We further demonstrated that FKA blocks the symmetric arginine dimethylation of histone H2A and H4 by binding to Y304 and F580 of PRMT5. Conclusions In summary, our research strongly suggests that PRMT5 is a potential epigenetic therapeutic target in bladder cancer, and that FKA can be used as a targeted inhibitor of PRMT5 for the treatment of bladder cancer.

We present a general finite element linearized Landau-Lifshitz-Gilbert equation (LLGE) solver for magnetic systems under weak time-harmonic excitation field. The linearized LLGE is obtained by assuming a small deviation around the equilibrium state of the magnetic system. Inserting such expansion into LLGE and keeping only first order terms gives the linearized LLGE, which gives a frequency domain solution for the complex magnetization amplitudes under an external time-harmonic applied field of a given frequency. We solve the linear system with an iterative solver using generalized minimal residual method. We construct a preconditioner matrix to effectively solve the linear system. The validity, effectiveness, speed, and scalability of the linear solver are demonstrated via numerical examples.

Abstract Background Integrin β4 (ITGB4) participates in tumorigenesis and progression of several malignancies, but its role and related mechanisms in clear cell renal cell carcinoma (ccRCC) remain unclear. Methods Quantitative real-time PCR (qRT-PCR), western blot and immunohistochemistry were used to detect mRNA and protein levels of relevant genes. Biological functions of ITGB4 and methyltransferase-like 14 (METTL14) were determined by in vitro and in vivo experiments. The levels of N6-methyladenosine (m6A) in ccRCC tissues and adjacent normal tissues were calculated via total RNA m6A quantification assay. The m6A modification of ITGB4 was demonstrated via m6A RNA immunoprecipitation (MeRIP), RIP and luciferase reporter assays. Results ITGB4 was significantly overexpressed in ccRCC tissues and high level of ITGB4 predicted poor prognosis as well as metastasis. Functionally, ITGB4 stimulated ccRCC cell migration and invasion in vitro and metastasis in vivo with epithelial–mesenchymal transition (EMT) strengthened. Mechanically, the total levels of m6A were reduced in ccRCC tissues. METTL14, a favorable factor for ccRCC patients’ prognosis, facilitated m6A modification on ITGB4 3′UTR and subsequently accelerated ITGB4 mRNA degradation, leading to its declined expression. Furthermore, the METTL14-mediated inhibition of ITGB4 expression was dependent on the YTH domain family protein 2 (YTHDF2), which acted as an m6A reader to bind to ITGB4 mRNA and to promote its decay. In addition, we demonstrated that knockdown of METTL14 promoted ccRCC cell migration, invasiveness and metastasis as well as stimulating the EMT process and the PI3K/AKT signal by overexpressing ITGB4. Conclusion Our study reveals that METTL14 inhibits ITGB4 expression via m6A modification to attenuate metastasis and EMT of ccRCC cells, suggesting the METTL14/ITGB4 axis as a potential prognostic biomarker and therapeutic target for ccRCC. Video Abstract

Dysregulation of deubiquitination has been reported to contribute to carcinogenesis. However, the function and mechanism of deubiquitinating enzyme 26S proteasome non‐ATPase regulatory subunit 14 (PSMD14) in the progression of ovarian cancer (OV), the deadliest gynecological cancer, still remains to be characterized. The present study demonstrated that PSMD14 was overexpressed in OV tissues and its higher levels correlated with a higher International Federation of Gynecology and Obstetrics (FIGO) stage in OV patients. A high level of PSMD14 expression was related to poor survival in OV patients. Knockdown and overexpression experiments elucidated that PSMD14 stimulated OV cell proliferation, invasion, and migration in vitro. Repression of PSMD14 suppressed OV tumor growth in vivo. PSMD14 inhibitor O‐phenanthroline (OPA) effectively attenuated malignant behaviors of OV cells in vitro and OV tumor growth in vivo. Mechanistically, we uncovered that PSMD14 was involved in post‐translational regulation of pyruvate kinase M2 isoform (PKM2). PSMD14 decreased K63‐linked ubiquitination on PKM2, downregulated the ratio of PKM2 tetramers to dimers and monomers, and subsequently diminished pyruvate kinase activity and induced nuclear translocation of PKM2, contributing to aerobic glycolysis in OV cells. Collectively, our findings highlight the potential roles of PSMD14 as a biomarker and therapeutic candidate for OV.