The functional brain network (FBN) serves as an important tool for investigating neurological and mental disorders. Unlike many traditional networks whose structures are often known in advance, FBNs need to be estimated from neuroimaging or electrophysiological data, and their quality generally determines the performance of downstream tasks, particularly in disorder detection. Recent studies have shown that high-order FBNs tend to achieve better discriminative performance, while some other work indicates that increasing the order of FBN does not necessarily bring additional gains in discriminative performance and may even lead to a rapid decline in discriminability. To fully leverage information across different-order FBNs and identify optimal order for downstream tasks, we design an adaptive high-order FBN fusion learning framework (AHFL) with attention mechanism for brain disorder detection. Specifically, we first construct a series of FBNs with continuously increasing orders and propose a data-driven approach to evaluate each order's contribution to the classification performance. The self-attention mechanism is employed to capture contextual dependencies during the sequential generation of multi-order FBNs, thereby offering a natural fusion approach. Experimental evidence shows that the proposed method achieves superior performance compared to the baseline. In particular, we find that third-order FBN is achieved the highest weights, playing a crucial role for the detection of autism spectrum disorder (ASD), whereas second-order FBN is the most effective for identifying patients with major depressive disorder (MDD). Our findings advance the identification of discriminative high-order FBNs and establish a generalizable diagnostic framework for brain disorders.

EEG-based emotion recognition is crucial for understanding human affective states, offering valuable insights into diverse fields like mental health monitoring and humancomputer interaction. Recent advancements in graph learning have significantly impacted EEG emotion recognition due to their ability to model the complex, dynamic relationships within brain networks. However, current methods often neglect the interplay between shared and individual correlations among EEG channels. Furthermore, individual variations in EEG patterns lead to distributional shifts that hinder the generalization of existing approaches. This paper proposes a novel Two-stage domain Adaptation with Hybrid Adaptive Graph learning (TAHAG) for EEG emotion recognition. TAHAG first employs hybrid adaptive graph learning to capture both shared and individual spatial characteristics of the EEG signals, dynamically integrating their contributions. Feature attention mechanisms are then incorporated to refine node features and enhance the model's discriminability. To address distributional variations, TAHAG utilizes a two-stage domain adaptation strategy. This strategy involves aligning the refined node features across different domains through discrepancy alignment. Subsequently, adversarial training captures domain-invariant summarized features of the entire graph. Extensive experiments on three public datasets demonstrate the superiority of TAHAG compared to existing methods. Furthermore, visualization of neuronal activity reveals significant brain regions and inter-channel relationships relevant to EEG emotion recognition.

This study introduces a liver fibrosis staging method based on the fusion of multi-modal imaging features. By leveraging ultrasound gray-scale images and ultrasound shear wave elastography (SWE), the method employs an attention-based weighted strategy to effectively fuse different feature maps, integrating information from diverse modalities. Additionally, it integrates multi-modal network branches and designs a multi- modal integrated loss function to update network parameters, thereby enhancing the model's generalization and anti- interference capabilities. The experimental results demonstrate that the proposed fusion network achieves a high AUC of 95.01 and an accuracy of 76.21%. Compared to existing liver fibrosis classification methods for the five-class classification task, which integrate multi-modal features from various liver imaging modalities, our approach shows a significant improvement of 6% in accuracy. With its lightweight model architecture and low computational resource consumption, the proposed method effectively performs liver fibrosis staging, holding significant promise for clinical auxiliary diagnosis.

Fatigue driving is a significant cause of road traffic accidents and associated casualties. Automatic assessment of driver drowsiness by monitoring electroencephalography (EEG) signals offer a more objective way to improve driving safety. However, most existing measures are based on multi-channel EEG signals, which are more difficult to apply in practical scenarios as it usually lacks better portability and comfort. In addition, due to the relatively parsimonious and non-stationary characteristics, it is still challenging to effectively accomplish drowsiness recognition by exploiting single-channel EEG signals alone. To this end, we propose a novel temporal-frequential attentional convolutional neural network (TFAC-Net) to take full advantage of spectral-temporal features for single-channel EEG driver drowsiness recognition. Specifically, to capture the potentially valuable information contained in single-channel EEG, the continuous wavelet transform is first employed to generate a corresponding spectral-temporal representation. Then, the temporal-frequential attention mechanism is adopted to reveal critical time-frequency regions in terms of the driver’s mental state. Finally, an adaptive feature fusion module is considered to recalibrate and integrate the most relevant feature channels for final prediction. Extensive experimental results on a widely used public EEG driving dataset demonstrate that the TFAC-Net approach is superior to the state-of-the-art methods, and could discover some discriminative temporal-frequential regions. Moreover, this study also sheds light on the development of portable EEG devices and practical driver drowsiness recognition.

Emotion recognition based on electroencephalography (EEG) has attracted significant attention and achieved considerable advances in the fields of affective computing and human-computer interaction. However, most existing studies ignore the coupling and complementarity of complex spatiotemporal patterns in EEG signals. Moreover, how to exploit and fuse crucial discriminative aspects in high redundancy and low signal-to-noise ratio EEG signals remains a great challenge for emotion recognition. In this paper, we propose a novel attention-based spatial-temporal dual-stream fusion network, named ASTDF-Net, for EEG-based emotion recognition. Specifically, ASTDF-Net comprises three main stages: first, the collaborative embedding module is designed to learn a joint latent subspace to capture the coupling of complicated spatiotemporal information in EEG signals. Second, stacked parallel spatial and temporal attention streams are employed to extract the most essential discriminative features and filter out redundant task-irrelevant factors. Finally, the hybrid attention-based feature fusion module is proposed to integrate significant features discovered from the dual-stream structure to take full advantage of the complementarity of the diverse characteristics. Extensive experiments on two publicly available emotion recognition datasets indicate that our proposed approach consistently outperforms state-of-the-art methods.

MOTIVATION: As the third-generation sequencing technology, nanopore sequencing has been used for high-throughput sequencing of DNA, RNA, and even proteins. Recently, many studies have begun to use machine learning technology to analyze the enormous data generated by nanopores. Unfortunately, the success of this technology is due to the extensive labeled data, which often suffer from enormous labor costs. Therefore, there is an urgent need for a novel technology that can not only rapidly analyze nanopore data with high-throughput, but also significantly reduce the cost of labeling. To achieve the above goals, we introduce active learning to alleviate the enormous labor costs by selecting the samples that need to be labeled. This work applies several advanced active learning technologies to the nanopore data, including the RNA classification dataset (RNA-CD) and the Oxford Nanopore Technologies barcode dataset (ONT-BD). Due to the complexity of the nanopore data (with noise sequence), the bias constraint is introduced to improve the sample selection strategy in active learning. Results: The experimental results show that for the same performance metric, 50% labeling amount can achieve the best baseline performance for ONT-BD, while only 15% labeling amount can achieve the best baseline performance for RNA-CD. Crucially, the experiments show that active learning technology can assist experts in labeling samples, and significantly reduce the labeling cost. Active learning can greatly reduce the dilemma of difficult labeling of high-capacity nanopore data. We hope active learning can be applied to other problems in nanopore sequence analysis. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/guanxiaoyu11/AL-for-nanopore. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In recent years, Electroencephalogram (EEG)-based emotion recognition has developed rapidly and gained increasing attention in the field of brain-computer interface. Relevant studies in the neuroscience domain have shown that various emotional states may activate differently in brain regions and time points. Though the EEG signals have the characteristics of high temporal resolution and strong global correlation, the low signal-to-noise ratio and much redundant information bring challenges to the fast emotion recognition. To cope with the above problem, we propose a Temporal and channel Transformer (TcT) model for emotion recognition, which is directly applied to the raw preprocessed EEG data. In the model, we propose a TcT self-attention mechanism that simultaneously captures temporal and channel dependencies. The sliding window weight sharing strategy is designed to gradually refine the features from coarse time granularity, and reduce the complexity of the attention calculation. The original signal is passed between layers through the residual structure to integrate the features of different layers. We conduct experiments on the DEAP database to verify the effectiveness of the proposed model. The results show that the model achieves better classification performance in less time and with fewer resources than state-of-the-art methods.