Baoji University of Arts and Sciences

m 1 A modification, as a pivotal RNA epigenetic modification, plays a central regulatory role in the pathogenesis and progression of complex human diseases, including cancer. Exploring the potential associations between m 1 As and diseases are an important approach to revealing the molecular mechanism of disease onset. However, traditional biological experiments have the limitations of time-consuming and labor-intensive, resulting in an extremely scarce amount of verified m 1 A-disease association data. Meanwhile, the existing computational prediction methods are mostly limited to specific application scenarios and rely solely on the direct correlation data between m 1 As and diseases. They do not fully integrate multi-dimensional biological information and thus are unable to achieve efficient and accurate association predictions. In view of this, this study proposes a method for predicting the association between m 1 A modification and diseases based on a ternary heterogeneous network and GCN. By introducing circRNA as an intermediate connection node, a ternary association network of m 1 A-circRNA-disease is constructed, which effectively enriches the dimension of feature information for both m 1 A and diseases. Meanwhile, leveraging the feature learning capability of Graph Convolutional Network, the extraction and representation of their features are realized. The experimental results demonstrate that the proposed approaches significantly outperforms existing mainstream methods in predictive performance, substantially enhancing the accuracy and reliability of m 1 A-disease association prediction. Furthermore, case validation has further confirmed that the predicted candidate m 1 A sites participate in regulating disease-related gene expression networks by modulating core processes such as RNA localization, stability, and translation efficiency, thereby providing novel insights into the investigation of disease pathogenesis.

To enhance the measurement accuracy of piezoresistive pressure sensors across a wide range of pressures and temperatures, this study proposes a temperature compensation model based on a Multi-strategy Fusion Improved Dung Beetle Optimization (MSDBO) algorithm. The model addresses three critical limitations of the traditional Dung Beetle Optimizer (DBO): (1) random population initialization that hinders effective environmental exploration, (2) a linear boundary convergence factor that weakens the global-local balance, and (3) susceptibility to local optima. To overcome these challenges, First, Maximin Latin Hypercube Sampling (MLHS) ensures uniform population distribution, enhancing convergence and compensation accuracy. Second, a nonlinear boundary convergence factor improves global search capability and accelerates convergence. Third, a dual-strategy combining beetle somersault foraging with adaptive Gaussian-Cauchy mutation prevents entrapment in local optima while boosting optimization capacity. The proposed MSDBO algorithm was applied to refine the weights and thresholds in a Back-Propagation Neural Network (BPNN) temperature compensation model. Practical implementation on an STM32F407VET6-based transmitter for 0–50 MPa/−20°C−70°C piezoresistive pressure sensors: temperature compensation reduced zero drift and sensitivity drift coefficients by one and three orders of magnitude, respectively, while improving full-scale accuracy from 6.36% to 0.041%—a two-order-of-magnitude enhancement.