Emotions can be perceived through the face, body, and whole-person, while previous studies on the abstract representations of emotions only focused on the emotions of the face and body. It remains unclear whether emotions can be represented at an abstract level regardless of all three sensory cues in specific brain regions. In this study, we used the representational similarity analysis (RSA) to explore the hypothesis that the emotion category is independent of all three stimulus types and can be decoded based on the activity patterns elicited by different emotions. Functional magnetic resonance imaging (fMRI) data were collected when participants classified emotions (angry, fearful, and happy) expressed by videos of faces, bodies, and whole-persons. An abstract emotion model was defined to estimate the neural representational structure in the whole-brain RSA, which assumed that the neural patterns were significantly correlated in within-emotion conditions ignoring the stimulus types but uncorrelated in between-emotion conditions. A neural representational dissimilarity matrix (RDM) for each voxel was then compared to the abstract emotion model to examine whether specific clusters could identify the abstract representation of emotions that generalized across stimulus types. The significantly positive correlations between neural RDMs and models suggested that the abstract representation of emotions could be successfully captured by the representational space of specific clusters. The whole-brain RSA revealed an emotion-specific but stimulus category-independent neural representation in the left postcentral gyrus, left inferior parietal lobe (IPL) and right superior temporal sulcus (STS). Further cluster-based MVPA revealed that only the left postcentral gyrus could successfully distinguish three types of emotions for the two stimulus type pairs (face-body and body-whole person) and happy versus angry/fearful, which could be considered as positive versus negative for three stimulus type pairs, when the cross-modal classification analysis was performed. Our study suggested that abstract representations of three emotions (angry, fearful, and happy) could extend from the face and body stimuli to whole-person stimuli and the findings of this study provide support for abstract representations of emotions in the left postcentral gyrus.

The functional magnetic resonance imaging (fMRI) is a noninvasive technique for studying brain activity, such as brain network analysis, neural disease automated diagnosis and so on. However, many existing methods have some drawbacks, such as limitations of graph theory, lack of global topology characteristic, local sensitivity of functional connectivity, and absence of temporal or context information. In addition to many numerical features, fMRI time series data also cover specific contextual knowledge and global fluctuation information. Here, we propose multi-scale time-series kernel-based learning model for brain disease diagnosis, based on Jensen-Shannon divergence. First, we calculate correlation value within and between brain regions over time. In addition, we extract multi-scale synergy expression probability distribution (interactional relation) between brain regions. Also, we produce state transition probability distribution (sequential relation) on single brain regions. Then, we build time-series kernel-based learning model based on Jensen-Shannon divergence to measure similarity of brain functional connectivity. Finally, we provide an efficient system to deal with brain network analysis and neural disease automated diagnosis. On Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our proposed method achieves accuracy of 0.8994 and AUC of 0.8623. On Major Depressive Disorder (MDD) dataset, our proposed method achieves accuracy of 0.9166 and AUC of 0.9263. Experiments show that our proposed method outperforms other existing excellent neural disease automated diagnosis approaches. It shows that our novel prediction method performs great accurate for identification of brain diseases as well as existing outstanding prediction tools.

Compound–protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound–protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.

Motivation: Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe–metabolite–disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed. Results: In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe–metabolite, metabolite–disease and disease–microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease–microbe–metabolite graph, we used the bipartite graph attention network on the disease–microbe and metabolite–microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease–metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson’s disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases. Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git

Attention is a crucial brain function for human beings. Using neuropsychological paradigms and task-based functional brain imaging, previous studies have indicated that widely distributed brain regions are engaged in three distinct attention subsystems: alerting, orienting and executive control (EC). Here, we explored the potential contribution of spontaneous brain activity to attention by examining whether resting-state activity could account for individual differences of the attentional performance in normal individuals. The resting-state functional images and behavioral data from attention network test (ANT) task were collected in 59 healthy subjects. Graph analysis was conducted to obtain the characteristics of functional brain networks and linear regression analyses were used to explore their relationships with behavioral performances of the three attentional components. We found that there was no significant relationship between the attentional performance and the global measures, while the attentional performance was associated with specific local regional efficiency. These regions related to the scores of alerting, orienting and EC largely overlapped with the regions activated in previous task-related functional imaging studies, and were consistent with the intrinsic dorsal and ventral attention networks (DAN/VAN). In addition, the strong associations between the attentional performance and specific regional efficiency suggested that there was a possible relationship between the DAN/VAN and task performances in the ANT. We concluded that the intrinsic activity of the human brain could reflect the processing efficiency of the attention system. Our findings revealed a robust evidence for the functional significance of the efficiently organized intrinsic brain network for highly productive cognitions and the hypothesized role of the DAN/ VAN at rest.