Arginine methylation-dependent cGAS stability promotes non-small cell lung cancer cell proliferation.

Cyclic GMP-AMP synthase (cGAS), promotes non-small cell lung cancer (NSCLC) cell proliferation. However, the specific mechanisms of cGAS-mediated NSCLC cell proliferation are largely unknown. In this study, we found asymmetric dimethylation by protein arginine methyltransferase 1 (PRMT1) at R127 of cGAS. This facilitated the binding of deubiquitinase USP7 and contributed to deubiquitination and stabilization of cGAS. PRMT1-and USP7-dependent cGAS stability, which also played a pivotal role in accelerating NSCLC cell proliferation through activating AKT pathway. We validated that the expression of cGAS and PRMT1 were positive correlated in human non-small cell lung cancer samples. Our study demonstrates a unique mechanism for managing cGAS stability by arginine methylation and indicates that PRMT1-cGAS-USP7 axis is a potential therapeutic target for NSCLC.

Speech extraction from vibration signals based on deep learning.

Extracting speech information from vibration response signals is a typical system identification problem, and the traditional method is too sensitive to deviations such as model parameters, noise, boundary conditions, and position. A method was proposed to obtain speech signals by collecting vibration signals of vibroacoustic systems for deep learning training in the work. The vibroacoustic coupling finite element model was first established with the voice signal as the excitation source. The vibration acceleration signals of the vibration response point were used as the training set to extract its spectral characteristics. Training was performed by two types of networks: fully connected, and convolutional. And it is found that the Fully Connected network prediction model has faster Rate of convergence and better quality of extracted speech. The amplitude spectra of the output speech signals (network output) and the phase of the vibration signals were used to convert extracted speech signals back to the time domain during the test set. The simulation results showed that the positions of the vibration response points had little effect on the quality of speech recognition, and good speech extraction quality can be obtained. The noises of the speech signals posed a greater influence on the speech extraction quality than the noises of the vibration signals. Extracted speech quality was poor when both had large noises. This method was robust to the position deviation of vibration responses during training and testing. The smaller the structural flexibility, the better the speech extraction quality. The quality of speech extraction was reduced in a trained system as the mass of node increased in the test set, but with negligible differences. Changes in boundary conditions did not significantly affect extracted speech quality. The speech extraction model proposed in the work has good robustness to position deviations, quality deviations, and boundary conditions.

Acoustic Insulation Characteristics and Optimal Design of Membrane-Type Metamaterials Loaded with Asymmetric Mass Blocks.

Membrane-type acoustic metamaterials (MAMs) are the focus of the current research due to their lightweight, small size, and good low-frequency sound insulation performance. However, there exists difficulties for extensive application because of the narrow sound insulation band. In order to achieve broadband sound isolation under the premise of lightweight, a novel MAM with asymmetric rings is firstly proposed in this paper. The sound transmission loss (STL) of this MAM is calculated by an analytical method and is verified by the finite element model. The different properties of the membrane when it is loaded with one, two, or four mass blocks are analyzed. The comparison with the traditional MAM proves the superior performance of this novel MAM. Moreover, by discussing the influence of the eccentricity and distribution position of the masses on the results, the tunability of the sound insulation performance of this MAM is proven. Finally, the Isight platform is used to optimize the MAM to further improve the broadband sound insulation performance: the average STL of the MAM is improved by 15.7%, the bandwidth above 30 dB is improved by 11.5%, and the mass density is reduced by 30.01%.

Damping Enhancement Using Axially Functionally Graded Porous Structure Based on Acoustic Black Hole Effect.

The acoustic black hole (ABH) effect for damping flexural waves using axially functionally graded porous (FGP) structure is investigated. With proposed power-law porosity of FGP structure, ABH can be achieved and damping effect is enhanced. The physics are explained from divergent conditions of the integrated wave phase at composite ends. Numerical results show the damping effect is increased with power law index. The phenomenon is expounded by the characteristics of reflection coefficient and impedance. It indicates that increasing power law index leads to smaller wavelength along to the end, then the wave needs more oscillation cycles to travel, which leads to more energy absorption. Transient analysis for 2D FGP structure also shows the focalization and ABH effect of the flexural waves.