Recent advancements in single-cell sequencing technologies have enabled researchers to identify cell subpopulations and their functional states with greater accuracy, thereby uncovering cellular heterogeneity. However, due to the heterogeneity across different single-cell multi-omics datasets and the intrinsic variability among cells, effectively integrating data from multiple molecular layers remains a significant challenge. To address this issue, a Single-cell Multi-level Synergistic Learning model (LoHi-SSL) is proposed, which integrates low-order and high-order information to achieve efficient multi-omics data fusion. LoHi-SSL consists of three key modules: low-order information learning, high-order information learning, and feature integration. The low-order information learning module focuses on addressing intra-omics cellular heterogeneity. It first extracts features from each omics dataset, then constructs a graph structure to capture intercellular relationships. A Graph Autoencoder is employed to extract local neighborhood information, effectively preserving intra-omics cellular similarity. The high-order information learning module is designed to eliminate cross-omics heterogeneity and align data in a unified latent representation space. To achieve this, multi-omics hypergraph learning is introduced to model complex cellular relationships across different omics, enhancing feature interactions.In the feature integration module, contrastive learning is utilized to guide the model in learning more discriminative feature representations by constructing positive and negative sample pairs. For different omics data from the same cell, LoHi-SSL encourages feature alignment within a shared latent space, reducing cross-omics heterogeneity. Meanwhile, for different cell types, a contrastive loss function is applied to increase the separation between their representations, thereby enhancing cellular distinguishability and achieving efficient single-cell multi-omics integration. Experimental results demonstrate that LoHi-SSL outperforms existing methods on six publicly available datasets, achieving superior performance in clustering tasks, particularly in terms of NMI (Normalized Mutual Information), ARI (Adjusted Rand Index), AMI (Adjusted Mutual Information), and ACC (Clustering Accuracy). Furthermore, robustness analysis shows that LoHi-SSL exhibits strong resistance to noise. Additionally, cell trajectory analysis using the latent representations learned by LoHi-SSL accurately reflects biological evolutionary pathways. In summary, LoHi-SSL provides an efficient and robust approach for single-cell multi-omics data integration, offering a powerful tool for studying cellular state transitions, heterogeneity, and regulatory mechanisms.

The discriminative feature is crucial for point cloud registration. Recent methods improve the feature discriminative by distinguishing between non-overlapping and overlapping region points. However, they still face challenges in distinguishing the ambiguous structures in the overlapping regions. Therefore, the ambiguous features they extracted resulted in a significant number of outlier matches from overlapping regions. To solve this problem, we propose a prior-guided SMoE-based registration method to improve the feature distinctiveness by dispatching the potential correspondences to the same experts. Specifically, we propose a prior-guided SMoE module by fusing prior overlap and potential correspondence embeddings for routing, assigning tokens to the most suitable experts for processing. In addition, we propose a registration framework by a specific combination of Transformer layer and prior-guided SMoE module. The proposed method not only pays attention to the importance of locating the overlapping areas of point clouds, but also commits to finding more accurate correspondences in overlapping areas. Our extensive experiments demonstrate the effectiveness of our method, achieving state-of-the-art registration recall (95.7%/79.3%) on the 3DMatch/3DLoMatch benchmark. Moreover, we also test the performance on ModelNet40 and demonstrate excellent performance.

Recently, there has been considerable exploration of methods for generating 3D point clouds, which is crucial for numerous 3D vision applications. Though conditional generation methods show promising performance, it depends on the additional paired label. On the other hand, unconditional generation methods usually fail to annotate the generated 3D point cloud. In this paper, we introduce a novel self-conditional architecture that trains on unlabeled data and then generates high-quality labeled 3D point clouds. Specifically, we design a module to extract geometry and view features, and then use a feature fusion module to integrate them as a substitute for label embedding in conditional point cloud generation. Then the point cloud generator is trained using the fused features. LPCG also harnesses CLIP to handle the view features of point clouds for generating label information. Besides, we train two feature diffusion modules to capture the essence of multimodal features and obtain diverse fused features for use as conditions in generating point clouds. Experiments on the ShapeNet dataset demonstrate that LPCG achieves state-of-the-art performance for single class generation. Our experimental results show that the accuracy of our generated label annotations reaches around 97.44% for a two-class generation task.

Single-cell multi-omics technologies enable simultaneous profiling of transcriptomic and epigenomic features, providing rich information for dissecting cellular heterogeneity and functional states. However, high dimensionality, modality-specific noise, and cross-modal heterogeneity pose significant challenges for integration and robust latent representation learning. In this study, we present MMIF-COAL, a multi-task framework for single-cell multiomics integration that jointly leverages clustering optimization and adversarial learning to achieve cross-modal feature alignment, enhanced latent space separability, and improved representation of rare cell types. We systematically evaluated MMIF-COAL on five publicly available single-cell multi-omics datasets. Experimental results demonstrate that MMIF-COAL consistently outperforms existing multi-task and single-task methods in clustering concordance and cell type classification accuracy, maintaining stable performance on large-scale and highly heterogeneous datasets. Ablation studies further confirm the contributions of KL divergence, entropy regularization, generator, discriminator, and data augmentation modules to the overall model performance. Analysis of cell type-specific latent features shows that MMIF-COAL effectively captures functional signals in highly active immune cells while maintaining low expression in resting or inactive cell types, demonstrating the biological interpretability of the learned latent representations. Overall, MMIF-COAL provides an efficient and robust solution for multi-task, multi-modal single-cell integration, offering valuable support for downstream analyses of cellular heterogeneity and multi-omics representations.

The scale of electric buses is experiencing rapid growth in the progress of electrification for public transport. While in many cities traditional charging mode is still adapted, which is highly dependent on charging piles deployed at fixed charging stations. However, due to the limited charging station areas and insufficient power supply, vehicles would suffer from long detours and queuing time, which would burden the sustainable operation of electric buses. To address these issues, integrating solar panels as on-board photovoltaic equipment to provide en-route power supply for electric buses has become a potential feasible solution to ease such pressures. Considering the impact of time varying light intensity and demand density, a continuous approximation model was constructed on the grid network, considering passengers' time costs, agency costs, and charging facilities costs and a mixed heuristic was designed to solve the model. Take a region in Panjin city, China as an example, the results indicate that: (1) On-board photovoltaic power supply can significantly improve the service level of electric buses without increasing system costs, and alleviate the dependence of electric buses on charging stations; (2) Increasing light intensity and coverage of solar panels have positive implications for reducing the number of charging piles and queuing time at stations. When light intensity exceeds 6.4×1041x, the charging queue time will be reduced by 97.32% with 100% coverage compared to 20% coverage.

Translation elongation is a crucial phase during protein biosynthesis. In this study, we develop a novel deep reinforcement learning-based framework, named Riboexp, to model the determinants of the uneven distribution of ribosomes on mRNA transcripts during translation elongation. In particular, our model employs a policy network to perform a context-dependent feature selection in the setting of ribosome density prediction. Our extensive tests demonstrated that Riboexp can significantly outperform the state-of-the-art methods in predicting ribosome density by up to 5.9% in terms of per-gene Pearson correlation coefficient on the datasets from three species. In addition, Riboexp can indicate more informative sequence features for the prediction task than other commonly used attribution methods in deep learning. In-depth analyses also revealed the meaningful biological insights generated by the Riboexp framework. Moreover, the application of Riboexp in codon optimization resulted in an increase of protein production by around 31% over the previous state-of-the-art method that models ribosome density. These results have established Riboexp as a powerful and useful computational tool in the studies of translation dynamics and protein synthesis. Availability: The data and code of this study are available on GitHub: https://github.com/Liuxg16/Riboexp. Contact:[email protected]; [email protected].