Accurate prediction of protein-ligand affinity (PLA) is critical for drug discovery. Recent deep learning approaches have adopted data-driven models for PLA prediction by learning intrinsic patterns from one-dimensional (1D) sequential or two-dimensional (2D) graph representations of proteins and ligands. However, these low-dimensional methods overlook the three-dimensional (3D) geometric features, which are hypothesized to be critical in binding interaction. To address the above problem, we present a Geometric deep learning approach with Hybrid message passing strategies--HybridGeo, for protein-ligand affinity prediction. We adopt dual-view graph learning to model the intra- and inter-molecular atomic interactions and propose to aggregate the spatial information with hybrid strategies. In addition, to fully model the inter-residue dependency upon message aggregation, we adopt a geometric graph transformer on the residue-scale graph of protein pockets. Extensive experiments on the PDBbind dataset show that HybridGeo achieves state-of-the-art performance with a Root Mean Square Error (RMSE) of 1.172. HybridGeo also achieves the best among all baseline models on three external test sets, showcasing good generalizability and robustness. Through systematic ablation experiments, we validated the effectiveness of the proposed modules, and further demonstrated the superior performance of HybridGeo in predicting the binding affinity of macrocyclic compound complexes through case studies. Visualization analysis further indicates the biological interpretability of the model predictions.

Recent advances have shown great promise in mining multimodal protein knowledge for better protein-protein interaction (PPI) prediction by enriching the representation of proteins. However, existing solutions lack a comprehensive consideration of both local patterns and global dependencies in proteins, hindering the full exploitation of modal information. Additionally, the inherent disparities between modalities are often disregarded, which may lead to inferior modality complementarity effects. To address these issues, we propose a cross-modality enhanced PPI prediction method from the perspectives of protein sequence and structure modalities, namely SSPPI. In this framework, our main contribution is that we integrate both sequence and structural modalities of proteins and employ an alignment and fusion method between modalities to further generate more comprehensive protein representations for PPI prediction. Specifically, we design two modal representation modules (Convformer and Graphormer) tailored for protein sequence and structure modalities, respectively, to enhance the quality of modal representation. Subsequently, we introduce a Cross-modality enhancer module to achieve alignment and fusion between modalities, thereby generating more informative modal joint representations. Finally, we devise a cross-protein fusion (CPF) module to model residue interaction processes between proteins, thereby enriching the joint representation of protein pairs. Extensive experimentation on four benchmark datasets demonstrates that our proposed model surpasses all current state-of-the-art (SOTA) methods. The source codes are publicly available at the following link https://github.com/bixiangpeng/SSPPI/.

In recent years, crowd counting has become an important issue in computer vision. In most methods, the density maps are generated by convolving with a Gaussian kernel from the ground-truth dot maps which are marked around the center of human heads. Due to the fixed geometric structures in CNNs and indistinct head-scale information, the head features are obtained incompletely. Deformable convolution is proposed to exploit the scale-adaptive capabilities for CNN features in the heads. By learning the coordinate offsets of the sampling points, it is tractable to improve the ability to adjust the receptive field. However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information. To handle the non-uniformed sampling, an improved Normed-Deformable Convolution (i.e.,NDConv) implemented by Normed-Deformable loss (i.e.,NDloss) is proposed in this paper. The offsets of the sampling points which are constrained by NDloss tend to be more even. Then, the features in the heads are obtained more completely, leading to better performance. Especially, the proposed NDConv is a light-weight module which shares similar computation burden with Deformable Convolution. In the extensive experiments, our method outperforms state-of-the-art methods on ShanghaiTech A, ShanghaiTech B, UCF_QNRF, and UCF_CC_50 dataset, achieving 61.4, 7.8, 91.2, and 167.2 MAE, respectively. The code is available athttps://github.com/bingshuangzhuzi/NDConv.

Air pollution has been long recognized as a hazardous factor for the human cardiovascular system. Sulfur dioxide (SO2) is a common ambient air pollutant that is able to cause detrimental effects on hearts. Though the cardiotoxicity effects by sulfur dioxide were well documented in epidemiological reports, however, the underlying mechanisms remain unclear owing to the technical limitations that exist in traditional experimental measures. In this article, we developed a multi-scale virtual ventricular tissue, which incorporated electrophysiological activities from subcellular to tissue levels and could provide comprehensive records and insightful mechanisms of the SO2induced ventricular arrhythmias. Based on the available cellular and molecular experimental data, our findings provide a rationale at tissue level in support of epidemiologic studies pointing to the deleterious effects of SO2pollution on cardiac function.

Academic emotions can produce a great impact on the learning effect. Normally, emotions are expressed externally in the students' facial expressions, speech and behaviour. In this paper, the focus is on automatic academic emotion inference based on facial expressions in online learning. Considering the lack of training samples for the inference algorithm, a spontaneous facial expression database is established. It includes the facial expressions of five common academic emotions and consists of two subsets: a video clip database and an image database. A total of 1,274 video clips and 30,184 images from 82 students are included in the database. The samples are labelled by both the participants and external coders. An extensive analysis is carried out on the image database using a convolutional neural network (CNN)‐based algorithm to infer self‐annotation. Some data augmentation algorithms are applied to improve the algorithm performance. Additionally, an adaptive data augmentation algorithm based on spatial transformer network is introduced, which can remove some confounding factors in the original images. The algorithm can obviously improve the inference performance, which has been proven by comparing some evaluation indicators before and after adoption. Such a database will certainly accelerate the application of affective computing in the educational field.

Collaborative learning has been widely used in educational contexts. Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method to obtain inter-homogeneous and intra-heterogeneous groups. In this method, the group formation problem is translated into a combinatorial optimization problem, and an improved genetic algorithm approach is also proposed to cope with this problem. To evaluate the proposed method, we carry out computational experiments based on eight datasets with different levels of complexity. The results show that the proposed approach is effective and stable for composing inter-homogeneous and intra-heterogeneous groups.