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To study the acoustic characteristics of sound scattered from laminated structures such as elastic plates and shells, it is usually required to solve the Lamb waves’ dispersion equations. Many traditional root-finding methods such as bisection, the Newton–Raphson method, and the Muller method are not able to tackle the problem completely. A simple but powerful method named local peaks search (LPS) is proposed to overcome their drawbacks. Firstly, the non-zero part of the dispersion equation is defined as the dispersion function, and its reciprocal is used to transform the zeros (i.e., roots) into local peaks. Secondly, the chosen complex domain is discretized, and the coarse local domains where the local peaks exist are determined by the direct search method globally. Thirdly, the Muller method is applied to obtain the refined locations of local peaks. Lastly, in order to refine the results, a hierarchical scheme is designed and the iteration of the above procedures is implemented; the error is set to be 10−16 as the stop criteria. The accuracy of the LPS method is validated by comparing it with the bisection method for the problem of elastic plates in the vacuum. The acoustic echo structures are analyzed experimentally. By computation of Lamb waves’ phase velocity, the critical angles are derived numerically and compared with the results acquired by an experiment using monostatic sound transducers. In this way, it is validated that the elastic scattered wave components are the highlights shown in the time-angle figure. Furthermore, the work can be applied for non-destructive testing, especially underwater structural health monitoring.

Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks (GNNs) have made significant progress in learning representations of protein-ligand complexes to estimate binding affinities. To improve the performance of GNNs, there frequently needs to look into protein-ligand complexes from geometric perspectives. While the “off-the-shelf” GNNs could incorporate some basic geometric structures of molecules, such as distances and angles, through modeling the complexes as homophilic graphs, these solutions seldom take into account the higher-level geometric attributes like curvatures and homology, and also heterophilic interactions.To address these limitations, we introduce the Curvature-based Adaptive Graph Neural Network (CurvAGN). This GNN comprises two components: a curvature block and an adaptive attention guided neural block (AGN). The curvature block encodes multiscale curvature informaton, then the AGN, based on an adaptive graph attention mechanism, incorporates geometry structure including angle, distance, and multiscale curvature, long-range molecular interactions, and heterophily of the graph into the protein-ligand complex representation. We demonstrate the superiority of our proposed model through experiments conducted on the PDBbind-V2016 core dataset.

Microbial methanogenesis in anaerobic soils contributes greatly to global methane (CH 4 ) release, and understanding its response to temperature is fundamental to predicting the feedback between this potent greenhouse gas and climate change. A compensatory thermal response in microbial activity over time can reduce the response of respiratory carbon (C) release to temperature change, as shown for carbon dioxide (CO 2 ) in aerobic soils. However, whether microbial methanogenesis also shows a compensatory response to temperature change remains unknown. Here, we used anaerobic wetland soils from the Greater Khingan Range and the Tibetan Plateau to investigate how 160 days of experimental warming (+4°C) and cooling (−4°C) affect the thermal response of microbial CH 4 respiration and whether these responses correspond to changes in microbial community dynamics. The mass-specific CH 4 respiration rates of methanogens decreased with warming and increased with cooling, suggesting that microbial methanogenesis exhibited compensatory responses to temperature changes. Furthermore, changes in the species composition of methanogenic community under warming and cooling largely explained the compensatory response in the soils. The stimulatory effect of climate warming on soil microbe-driven CH 4 emissions may thus be smaller than that currently predicted, with important consequences for atmospheric CH 4 concentrations. Soil microbes produce more methane as temperatures warm, but it is unclear if they acclimate to heat, or keep producing more of the greenhouse gas. Here the authors use artificial wetland warming experiments to show that after initial spikes in methane emissions after warming, emissions level out over time.

Nucleosomes composed of histone octamer and DNA are the basic structural unit in the eukaryote chromosome. Under the stimulation of various factors, histones will undergo posttranslational modifications such as methylation, phosphorylation, acetylation, and ubiquitination, which change the three-dimensional structure of chromosomes and affect gene expression. Therefore, the combination of different states of histone modifications modulates gene expression is called histone code. The formation of learning and memory is one of the most important mechanisms for animals to adapt to environmental changes. A large number of studies have shown that histone codes are involved in the formation and consolidation of learning and memory. Here, we review the most recent literature of histone modification in regulating neurogenesis, dendritic spine dynamic, synapse formation, and synaptic plasticity.

In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

Forest soils, serving as an important sink for atmospheric methane (CH 4 ), modulate the global CH 4 budget. However, the direction and magnitude of the forest soil CH 4 sink under warming remain uncertain, partly because the temperature response of microbial CH 4 oxidation varies substantially across geographical scales. Here, we reveal the spatial variation in the response of forest soil microbial CH 4 oxidation to warming, along with the driving factors, across 84 sites spanning a broad latitudinal gradient in eastern China. Our results show that the temperature sensitivity of soil microbial CH 4 oxidation significantly declines with increasing site mean annual temperature, with a range of 0.03 to 0.77 μg CH 4 g –1 soil d –1  °C –1 . Moreover, soil resources and type II methanotrophs play crucial roles in shaping the temperature sensitivity of soil microbial CH 4 oxidation. Our findings highlight the importance of incorporating climate, soil resources, and methanotroph groups into biogeochemical models to more realistically predict forest soil CH 4 sink under warming. Forest soils are vital in regulating the atmospheric CH 4 budget, but their response to warming varies spatially. The authors assess the temperature response of soil microbial CH 4 oxidation across 84 forest sites in China, finding higher temperature sensitivity in colder regions, indicating greater CH 4 sink potential in these areas with warming.

Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern and feature information are different from most normal graphs in a graph set, which is rarely studied by other researchers but has significant application value. For instance, GLAD can be used to distinguish some different characteristic molecules in drug discovery and chemical analysis. However, GLAD mainly faces the following three challenges: (1) learning more comprehensive graph level representations to differ normal graphs and abnormal graphs, (2) designing an effective graph anomaly evaluation paradigm to capture graph anomalies from the local and global graph perspectives, (3) overcoming the number imbalance problem of normal and abnormal graphs. In this paper, we combine graph neural networks and contrastive learning to build an end-to-end GLAD framework for solving the three challenges above. We aim to design a new graph level anomaly evaluation way, which first utilizes the contrastive learning strategy to enhance different level representations of normal graphs from node and graph levels by a graph convolution autoencoder with perturbed graph encoder. Then, we evaluate the error of them with corresponding representations of the generated reconstruction graph to detect anomalous graphs. Extensive experiments on ten real-world datasets from three areas, such as molecular, protein and social network anomaly graphs, show that our model can effectively detect graph level anomaly from the majority and outperform existing advanced methods.

Motivation The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy. Results Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

Background Glutathione-S transferases (GSTs) comprise a series of critical enzymes involved in detoxification of endogenous or xenobiotic compounds. Among several GSTs, Glutathione S-transferases mu (GSTM) has been implicated in a number of cancer types. However, the prognostic value and potential functions of the GSTM family genes have not been investigated in lung adenocarcinoma (LUAD). Methods We examined the expression of GSTM5 in LUAD and identified associations among GSTM5 expression, clinicopathological features, survival data from the Cancer Genome Atlas (TCGA). The correlation between GSTM5 DNA methylation and its expression was analyzed using the MEXPRESS tool and UCSC Xena browser. The methylation status of GSTM5 in the promoter region in lung cancer cells was measured by methylation-specific PCR (MSP). After 5-aza-2'-deoxycytidine treatment of lung cancer cells, expression of GSTM5, cell proliferation and migration were assessed by RT-PCR, CCK-8 and transwell assays, respectively. Results The results showed that GSTM5 was abnormally down-regulated in LUAD patients’ tissues, and patients with low GSTM5 expression level had significantly shorter OS. Cox regression analyses revealed that GSTM5 was associated with overall survival (OS) of LUAD patients, which expression was an independent prognostic indicator in terms of OS (hazard ratio: 0.848; 95% CI: 0.762–0.945; P  = 0.003). In addition, we found the promoter region of GSTM5 was hypermethylated in the tumor tissue compared with adjacent normal tissues, and the average methylation level of GSTM5 were moderately correlated with its expression. Moreover, methylation-specific PCR also showed that the GSTM5 gene promoter was hypermethylated in lung cancer cells, and treatment with 5-Aza-CdR can restore the gene expression and inhibit cell proliferation and migration. Finally, Gene Set Enrichment Analysis (GSEA) revealed that low GSTM5 expression was significantly related to DNA repair pathways. Conclusion Our data demonstrate that low GSTM5 expression and its high DNA methylation status may act as a novel putative molecular target gene for LUAD.

The rotational speed vectors of the bearing balls affect their service life and running performance. Observing the actual rotational speed of the ball is a prerequisite for revealing its true motion law and conducting sliding behavior simulation analysis. To address the need for accuracy and real-time measurement of spin angular velocity, which is also under high-frequency and high-speed ball motion conditions, a new measurement method of ball rotation vectors based on a binocular vision system is proposed. Firstly, marker points are laid on the balls, and their three-dimensional (3D) coordinates in the camera coordinate system are calculated in real time using the triangulation principle. Secondly, based on the 3D coordinates before and after the movement of the marker point and the trajectory of the ball, the mathematical model of the spin motion of the ball was established. Finally, based on the ball spin motion model, the three-dimensional vision measurement technology was first applied to the measurement of the bearing ball rotation vector through formula derivation, achieving the analysis of bearing ball rolling and sliding characteristics. Experimental results demonstrate that the visual measurement system with the frame rate of 100 FPS (frames per second) yields a measurement error within ±0.2% over a speed range from 5 to 50 RPM (revolutions per minute), and the maximum measurement errors of spin angular velocity and linear velocity are 0.25 °/s and 0.028 mm/s, respectively. The experimental results show that this method has good accuracy and stability in measuring the rotation vector of the ball, providing a reference for bearing balls’ rotational speed monitoring and the analysis of the sliding behavior of bearing balls.