MOTIVATION: 5-Methylcytosine (5mC) plays an important role in gene regulation and development. Although nanopore sequencing has enabled direct detection of 5mC, existing methods still face several limitations, including poor generalization across species and sequence contexts (CpG/CHG/CHH), as well as suboptimal integration of sequence and current signals. RESULTS: Here, we present MethyNano, a deep learning framework incorporating a contrastive learning strategy to detect 5mC from nanopore reads. By encouraging more discriminative and stable representations, the contrastive objective improves the model's sensitivity to rare sequence contexts and reduces its prediction uncertainty in challenging regions. Across datasets from Arabidopsis thaliana, Oryza sativa, and Homo sapiens, our model achieves superior performance on key metrics compared with other existing methods. Extensive cross-species and cross-motif experiments demonstrate the robust generalization performance of MethyNano, while dimensionality-reduction visualizations of learned features provide an intuitive view of the model's efficient representation capability. Moreover, our ablation studies show that MethyNano's architecture enables more effective integration of critical features, leading to higher predictive accuracy. AVAILABILITY AND IMPLEMENTATION: The project code is available at https://github.com/baigeHUI/MethyNano and https://doi.org/10.5281/zenodo.19858400.

Abstract Path planning can guarantee that agents reach their goals without colliding with obstacles and other agents in an optimal way and it is a very important component in the research of crowd simulation. In this article, we propose a novel path planning approach for multiple agents which combines soft actor critic (SAC) algorithm and curriculum learning to solve the problems of single policy, slow convergence of the policy in an unknown environment with sparse rewards. The path planning task is set as lessons from easy to difficult, and the neural network of the SAC algorithm is arranged to learn in sequence, and finally the neural network can be fully competent for the path planning task. We also stack the state information to address the problems caused by limited observation for policy learning, and design a comprehensive reward function to make agents reach their goals successfully and avoid collisions with static obstacles and other agents. The experimental results demonstrate that our approach can plan smooth and natural paths for multiple agents, and furthermore, our model has a certain generalization ability and a better adaptability to the changes in a dynamic environment.

The main melody extraction of polyphonic music is a challenging task for music information retrieval. Traditional convolutional neural networks, recurrent neural networks have effectively improved this task. In recent years, with the development of attention mechanism in neural networks, the frequency and time attention information of audio has been fully exploited, and the amplitude properties of audio can also be better integrated with a good fusion module. This paper improves the frequency-temporal attention based on others’ prior work. By extracting the attention information with the frequency-temporal attention and performing additive fusion of features, the combined frequency-temporal attention is obtained. Then we apply attentional feature fusion based on multi-scale channel attention, and finally the temporal dependencies are learned through the self-attention module. Our experimental results on four datasets demonstrate that our model outperforms existing models.

The hybrid wireless and power line communication (HWPLC) networks address the problem that mobile wireless sensors and power line communication (PLC) sensors cannot communicate with each other within an Internet of Things (IoT) network. In this paper, we design a relay equipped with a dual wireless and PLC interface, which connects both the PLC and wireless sensors into an IoT network. Furthermore, the dual-interface relay forwards messages by adaptively selecting a interface according to the channel state. A general mathematical probability model of the dual-interface relaying system is presented. The probability density function of the output signal-to-noise ratio (SNR) is developed, which is based on explicit closed-form expressions derived from the statistics character of the PLC and wireless channel. Furthermore, the average capacity, bit-error rate (BER) expressions, and the outage probability formulas are derived. Numerical results show that the HPLWC relaying system with the dual-interface can significantly improve the performance of capacity, BER, and outage probability by adaptively selecting the interface with the optimal received SNR.