The evolution of artificial intelligence (AI) has driven AI Agents to shift from passive response to proactive interaction, a critical advancement for optimizing user experience and operational efficiency. Current research predominantly emphasizes functional implementation, while studies on user attention and related task performance of proactive interaction mechanisms remain relatively scarce. This research systematically examines three core characteristics of AI Agent proactive interaction (environmental perception, decision support, and task execution) through an integrated methodology combining eye-tracking, Information Processing Efficiency Index, and Self-Assessment Manikin scales. Key findings reveal: (1) Proactive environmental perception with visual-textual cues significantly enhances user attention and task efficiency; (2) Provision of decision-making rationales strengthens user trust and satisfaction; (3) Proactive information delivery improves both user satisfaction and information processing efficiency. As the first study to jointly analyze AI Agent interaction mechanisms from psychological and cognitive perspectives, this work establishes theoretical foundations and actionable guidelines for designing intelligent assistive systems.

SUMMARY: Fungi form a large and heterogeneous group of eukaryotic organisms with diverse ecological niches. The high importance of fungi contrasts with our limited understanding of fungal lifestyle and adaptability to environment. Over the last decade, the high-throughput sequencing technology produced tremendous RNA-sequencing (RNA-seq) data. However, there is no comprehensive database for mycologists to conveniently explore fungal gene expression and alternative splicing. Here, we have developed FungiExp, an online database including 35 821 curated RNA-seq samples derived from 220 fungal species, together with gene expression and alternative splicing profiles. It allows users to query and visualize gene expression and alternative splicing in the collected RNA-seq samples. Furthermore, FungiExp contains several online analysis tools, such as differential/specific, co-expression network and cross-species gene expression conservation analysis. Through these tools, users can obtain new insights by re-analyzing public RNA-seq data or upload personal data to co-analyze with public RNA-seq data. AVAILABILITY AND IMPLEMENTATION: The FungiExp is freely available at https://bioinfo.njau.edu.cn/fungiExp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Electroencephalogram (EEG) is an important technology to explore the central nervous mechanism of tinnitus. However, it is hard to obtain consistent results in many previous studies for the high heterogeneity of tinnitus. In order to identify tinnitus and provide theoretical guidance for the diagnosis and treatment, we propose a robust, data-efficient multi-task learning framework called Multi-band EEG Contrastive Representation Learning (MECRL). In this study, we collect resting-state EEG data from 187 tinnitus patients and 80 healthy subjects to generate a high-quality large-scale EEG dataset on tinnitus diagnosis, and then apply the MECRL framework on the generated dataset to obtain a deep neural network model which can distinguish tinnitus patients from the healthy controls accurately. Subject-independent tinnitus diagnosis experiments are conducted and the result shows that the proposed MECRL method is significantly superior to other state-of-the-art baselines and can be well generalized to unseen topics. Meanwhile, visual experiments on key parameters of the model indicate that the high-classification weight electrodes of tinnitus' EEG signals are mainly distributed in the frontal, parietal and temporal regions. In conclusion, this study facilitates our understanding of the relationship between electrophysiology and pathophysiology changes of tinnitus and provides a new deep learning method (MECRL) to identify the neuronal biomarkers in tinnitus.

Abstract User tags in social network are valuable information for many applications such as Web search, recommender systems and online advertising. Thus, extracting high quality tags to capture user interest has attracted many researchers’ study in recent years. Most previous studies inferred users’ interest based on text posted in social network. In some cases, ordinary users usually only publish a small number of text posts and text information is not related to their interest very much. Compared with famous user, it is more challenging to find non-famous (ordinary) user’s interest. In this paper, we propose a probabilistic topic model,Bi-Labeled LDA,to automatically find interest tags for non-famous users in social network such as Twitter. Instead of extracting tags from text posts, tags of non-famous users are inferred from interest topics of famous users. With the proposed model, the formulation of social relationship between non-famous users and famous user is simulated and interest tags of famous users are exploited to supervise the training of the model and to make use of latent relation among famous users. Furthermore, the influence of popularity of famous user and popular tags are considered, and tags of non-famous users are ranked based on random walk model. Experiments were conducted on Twitter real datasets. Comparison with state-of-the-art methods shows that our method is more superior in terms of both ranking and quality of the tagging results.