Near-fault ground motions significantly threaten bridges due to their distinct features, which are often inadequately considered in current seismic codes based mainly on far-field records. This study analyzes 941 near-fault records to evaluate the effects of site class, pulse-like motions, and vertical components on the peak acceleration ratio and normalized response spectra. A finite element model of a typical simply supported girder bridge is developed to examine how these factors affect pier internal forces. Results show that the peak acceleration ratio increases with softer sites and exhibits large scatter in near-fault regions, indicating that the conventional vertical-to-horizontal ratio of 0.65 may significantly underestimate vertical seismic actions. Pulse motions shift and broaden response spectra, raising seismic demands for medium- to long-period structures. Additionally, pulse effects combined with soft sites cause coupled amplification of internal forces. This work offers a theoretical basis for seismic design and assessment of similar bridges.

DNA methylation is an epigenetic modification that plays a crucial role in genome stability and cellular specialization, essential for maintaining normal cellular function and development, also a manifestation indicator of some diseases. Various tools have been proposed for methylation detection, typically leveraging a third-generation sequencing technology called nanopore sequencing, which provides more accurate DNA sequencing data. However, existing tools have their own limitations and advantages in terms of computational resources and information processing, without achieving a good balance. In this situation, we developed EDNTOM (Ensemble Deep Network Tool Of Methylation), a DNA methylation detection tool based on deep learning technology. We employed ensemble learning techniques, integrating predictions from multiple pretrained single models, and introduced an attention weight mechanism to provide accurate and reliable detection, reducing the consumption of computational resources. Results demonstrate that EDNTOM outperforms individual models. Additionally, in cross-species transfer experiments, EDNTOM exhibits strong transfer learning capabilities. We hope this work can provide a more powerful and reliable solution for methylation detection, contributing to the fields of biological science and medicine. The project code is available at https://github.com/ViceMusic/EDNTOM.