Compressed video quality enhancement (CVQE) is crucial for mitigating compression artifacts and improving perceptual visual quality, especially under diverse quantization parameters (QPs) and motion patterns. However, many existing approaches insufficiently exploit long-range temporal dependencies, and their reliance on QP-specific training often leads to limited robustness when compression conditions change. In this work, we propose a just noticeable difference (JND)-aware and perception-driven learning framework for CVQE, termed the Pairwise Spatio-Temporal Alignment Network (PSTAN). PSTAN incorporates perceptual priors primarily through a JND-guided training paradigm rather than relying solely on architectural modifications, where learning is driven by perceptuallypoorvideo segments identified in the VideoSet dataset. This strategy alleviates the reliance on QP-specific supervision and promotes more stable enhancement behavior across varying compression conditions. To effectively capture temporal dependencies, PSTAN employs a pairwise spatio-temporal interaction mechanism that models each reference-target frame pair independently, enabling adaptive utilization of both nearby and distant frames. In addition, a transformer-based alignment module combining temporal mutual attention with cascaded deformable convolution is introduced to handle complex and large motions. Extensive experiments on VideoSet, MFQE 2.0 and our constructed HEVC-comperssed dataset show that PSTAN achieves consistent improvements over state-of-the-art CVQE methods in both objective and perceptual quality metrics. The code of this work is available at https://github.com/leryong/PSTAN.git.

Predicting microbe-drug associations (MDAs) is vital for accelerating drug discovery and optimizing clinical interventions in biomedical research. Traditional laboratory-based methods, though reliable, are constrained by high costs and limited scalability. While many computational approaches have utilized feature similarities to infer MDAs, they often overlook the complex and heterogeneous relationships inherent in biological networks, as well as the challenge posed by imbalanced datasets. In this study, we propose HGBHAN, a novel framework for MDAs prediction using heterogeneous graphs and bidirectional long short-term memory (Bi-LSTM) with hierarchical attention, for robust MDAs prediction. HGBHAN constructs a comprehensive heterogeneous network by integrating microbe and drug similarities with known association information, capturing multi-level structural and sequential dependencies. The model employs Bi-LSTM modules and a hierarchical attention mechanism to learn discriminative node embeddings, while residual connections are incorporated to address the over-smoothing issue in graph neural networks. Extensive experiments conducted on three public benchmark datasets demonstrate that HGBHAN outperforms existing models across multiple evaluation metrics, validating its efficacy in accurately predicting microbe-drug associations.

In the field of biomedicine, predicting microbe-drug associations (MDAs) is crucial for advancing drug discovery and personalized therapy. However, traditional experimental approaches often fall short in meeting requirements for accuracy and scalability. Previous studies have primarily relied on feature similarities to predict microbe-drug associations, largely ignoring the complex interdependencies essential for improved prediction. In this paper, we propose a novel framework named Deep Residual Learning Framework Using Heterogeneous Network Feature (DRL-HNet) for MDAs prediction. DRL-HNet constructs a heterogeneous network representation by integrating relationships and features from multiple data sources for both microbes and drugs. The model incorporates deep residual learning with bottleneck layers to effectively reduce computational complexity while enhancing network expressiveness. Multi-source feature fusion is leveraged to capture complex interaction patterns, while residual connections mitigate overfitting and enhance training efficiency. Extensive cross-validation experiments demonstrate that DRL-HNet outperforms existing models across multiple evaluation metrics, validating its efficacy in accurately predicting microbe-drug associations.

Central nervous system (CNS) diseases such as glioblastoma (GBM), multiple sclerosis (MS), and Alzheimer's disease (AD) remain challenging due to their complexity and limited treatments. Conventional drug repurposing strategies often rely on bulk RNA sequencing data, which can overlook cellular heterogeneity and mask rare but critical cell populations. Here, we introduce scDrugLink, a computational method that integrates single-cell transcriptomic data with drug targets and perturbation signatures to improve repurposing. For each cell type, scDrugLink constructs a Drug2Cell matrix based on drug targets to estimate promotion/inhibition scores and derives sensitivity/resistance scores by reverse matching signatures and disease-associated genes. These scores are then "linked", yielding robust therapeutic rankings. In our study, we present a systematic evaluation of single-cell drug repurposing methods for CNS diseases. Applied to atlas data for GBM, MS, and AD, scDrugLink surpassed three state-of-the-art methods (ASGARD, DrugReSC, and scDrugPrio), achieving area under the receiver operating characteristic curve (AUC) ranges of 0.6286-0.7242 and area under the precision-recall curve (AUPRC) ranges of 0.3412-0.5484. It also ranked top when comparing AUC and AUPRC at the level of individual cell types. Moreover, applying the "linking" principle to baseline methods boosted their performance, on average improving AUC and AUPRC by 0.0160 and 0.0244, respectively. Despite the advancements, the complexity and heterogeneity of CNS diseases, along with incomplete drug data, indicate that further improvement is necessary. We discuss these challenges and suggest directions for enhancing single-cell drug repurposing in the future.

Currently, deep neural networks can be extremely effective in robotic tactile perception. However, a major challenge is to solve the problem of continual learning of robotic tactile perception in an open and dynamic environment. In this paper, we propose a novel continual learning method for the domian incremental learning task in the field of tactile perception. To be specific, we introduce a morphology-specific variational autoencoders which can mitigate catastrophic forgetting by generating pseudo-samples for training in the continual learning process. We integrate the generative model and the discriminative model into one model, which reduces the size of model and improves the continual learning ability. In addition, considering the ordinal information between the hardness levels, we propose to add conditional information to the model and introduce a modified loss function to combine the latent value with the hardness information, which improves the continual learning performance by controlling the distribution and quality of pseudo-sample generation. Following this, we designed a tactile robot experiment, collected hardness data, and tested our model on this object hardness recognition task. We show experimentally that, after training, the model can still maintain the accuracy of more than 94% after learning three tasks in terms. Note to Practitioners—In the field of robotics tactile perception, the issue of continual learning in robots is a crucial problem that urgently requires resolution. We hope robots to effectively engage in continual learning across multiple tasks, ensuring the acquisition of new knowledge while mitigating the risk of forgetting previously acquired knowledge. In this paper, we propose a novel continual learning method for the domian incremental learning task. we introduce a morphology-specific variational autoencoders based on replaying pseudo-samples during continual learning process which reduces the size of model and improves the continual learning ability. We enhance model performance by integrating generative and discriminative models, incorporating conditional information to control the distribution of replayed sample types, and leveraging sequential relationships among samples. It is proved that the proposed method is able to effectively improve the accuracy in a tactile domian incremental learning task.

Predicting potential microbe-drug associations (MDA) can help study pathogenesis, expedite pharmaceutical innovation, and enhance targeted therapeutics. Given the time and labor intensity of traditional biological experiments, an increasing number of computational approaches are being employed to predict MDA. The method based on graph embedding is one of the most widely used. However, most of these methods only consider node embedding or graph structure information in isolation, which leads to restricted predictive accuracy. In this work, we propose a method called exploring microbe-drug association prediction via multi-attribute dual-decoder graph autoencoder (MDGAEMDA). Specifically, a heterogeneous network containing microbe similarity, drug similarity, and known associations is constructed. Second, to enrich the node information, the multi-attribute features are obtained by importing the topological information of microbe and drug. Then, two heterogeneous networks constructed by the graph masking strategy are input into dual-decoder graph autoencoder that contains one encoder and two decoders (node decoder and structure decoder) to learn both node embedding and graph structure information. Finally, two low-dimensional features are spliced into the features of MDA pairs and predicted by random forest. The model was compared with multiple advanced methods using public datasets. The experimental outcomes showed that our model significantly outperformed other methods. The case study of widely used drugs demonstrated the reliability of the proposed method to predict MDA.

Prostate cancer screening often relies on cost-intensive MRIs and invasive needle biopsies. Transrectal ultrasound imaging, as a more affordable and non-invasive alternative, faces the challenge of high inter-class similarity and intra-class variability between benign and malignant prostate cancers. This complexity requires more stringent differentiation of subtle features for accurate auxiliary diagnosis. In response, we introduce the novel Deep Augmented Metric Learning (DAML) network, specifically tailored for ultrasound-based prostate cancer classification. The DAML network represents a significant innovation in the metric learning space, introducing the Semantic Differences Mining Strategy (SDMS) to effectively discern and represent subtle differences in prostate ultrasound images, thereby enhancing tumor classification accuracy. Additionally, the DAML network strategically addresses class variability and limited sample sizes by combining the Linear Interpolation Augmentation Strategy (LIAS) and Permutation-Aided Reconstruction Loss (PARL). This approach enriches feature representation and introduces variability with straightforward structures, mirroring the efficacy of advanced sample generation techniques. We carried out comprehensive empirical assessments of the DAML model by testing its key components against a range of models, ensuring its effectiveness. Our results demonstrate the enhanced performance of the DAML model, achieving classification accuracies of 0.857 and 0.888 for benign and malignant cancers, respectively, underscoring its effectiveness in prostate cancer classification via medical imaging.

The identification of drug-target interactions (DTIs) is an essential step in drug discovery. In vitro experimental methods are expensive, laborious, and time-consuming. Deep learning has witnessed promising progress in DTI prediction. However, how to precisely represent drug and protein features is a major challenge for DTI prediction. Here, we developed an end-to-end DTI identification framework called BINDTI based on bi-directional Intention network. First, drug features are encoded with graph convolutional networks based on its 2D molecular graph obtained by its SMILES string. Next, protein features are encoded based on its amino acid sequence through a mixed model called ACmix, which integrates self-attention mechanism and convolution. Third, drug and target features are fused through bi-directional Intention network, which combines Intention and multi-head attention. Finally, unknown drug-target (DT) pairs are classified through multilayer perceptron based on the fused DT features. The results demonstrate that BINDTI greatly outperformed four baseline methods (i.e., CPI-GNN, TransfomerCPI, MolTrans, and IIFDTI) on the BindingDB, BioSNAP, DrugBank, and Human datasets. More importantly, it was more appropriate to predict new DTIs than the four baseline methods on imbalanced datasets. Ablation experimental results elucidated that both bi-directional Intention and ACmix could greatly advance DTI prediction. The fused feature visualization and case studies manifested that the predicted results by BINDTI were basically consistent with the true ones. We anticipate that the proposed BINDTI framework can find new low-cost drug candidates, improve drugs' virtual screening, and further facilitate drug repositioning as well as drug discovery.

Predicting accurately the mechanisms of drug-drug interaction (DDI) events is crucial in drug research and development. Existing methods used to predict these events are primarily based on deep learning and have achieved satisfactory results. However, they rarely consider the presence of redundant co-information between the multimodal data of a drug and the need for consistency in the predicted features of each drug modality. Herein, we propose a new method for drug interaction event prediction based on multimodal mutual orthogonal projection and intermodal consistency loss. Our method obtains the features of each modality through a multimodal mutual orthogonal projection module, which eliminates redundant common information with other modalities. In addition, we use the consistency loss between modalities and make the predicted features of each modality more similar. In comparative experiments, our proposed method achieves a prediction accuracy of 0.9500, and an area under the precision-recall (AUPR) curve is 0.9833 for known DDIs. This method outperforms existing methods. The results show that the proposed method is capable of accurately predicting DDIs.

Evolutionary multitasking (EMT), which aims to exploit effective knowledge among similar tasks to improve search efficiency, is a hot research topic that has recently attracted a lot of attention. Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ant species, is a popular and powerful search algorithm for NP-hard combinatorial optimization problems. However, EMT has seldom been integrated with the ACO. Inspired by the remarkable success of multitasking evolutionary algorithms in numerous research fields, this paper proposes a multitasking ant colony optimization framework (MTACO). The proposed framework enables ants to exploit the pheromones of ant colonies with similar tasks through certain conditions to improve the efficiency and the quality of results of ACO when processing multiple similar tasks dynamically. Furthermore, a multitasking ACS (MTACS) is implemented based on the proposed MTACO framework to solve dynamic vehicle path planning problems (DVPP). The experimental results on DVPP have verified that MTACO can improve the performance of ACO in terms of both algorithm efficiency and quality of results, when ACO is handling multiple tasks simultaneously.

Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying the associations between human diseases and circRNA can help in disease prevention, diagnosis and treatment. Traditional methods are time consuming and laborious. Meanwhile, computational models can effectively predict potential circRNA-disease associations (CDAs), but are restricted by limited data, resulting in data with high dimension and imbalance. In this study, we propose a model based on automatically selected meta-path and contrastive learning, called the MPCLCDA model. First, the model constructs a new heterogeneous network based on circRNA similarity, disease similarity and known association, via automatically selected meta-path and obtains the low-dimensional fusion features of nodes via graph convolutional networks. Then, contrastive learning is used to optimize the fusion features further, and obtain the node features that make the distinction between positive and negative samples more evident. Finally, circRNA-disease scores are predicted through a multilayer perceptron. The proposed method is compared with advanced methods on four datasets. The average area under the receiver operating characteristic curve, area under the precision-recall curve and F1 score under 5-fold cross-validation reached 0.9752, 0.9831 and 0.9745, respectively. Simultaneously, case studies on human diseases further prove the predictive ability and application value of this method.

The elucidation of gene regulatory networks (GRNs) is one of the central challenges of systems biology, which is crucial for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but identifying redundant regulation remains a fundamental problem. Although considering topological properties and edge importance measures simultaneously can identify and reduce redundant regulations, how to address their respective weaknesses whilst leveraging their strengths is a critical problem faced by researchers. Here, we propose a network structure refinement method for GRN (NSRGRN) that effectively combines the topological properties and edge importance measures during GRN inference. NSRGRN has two major parts. The first part constructs a preliminary ranking list of gene regulations to avoid starting the GRN inference from a directed complete graph. The second part develops a novel network structure refinement (NSR) algorithm to refine the network structure from local and global topology perspectives. Specifically, the Conditional Mutual Information with Directionality and network motifs are applied to optimise the local topology, and the lower and upper networks are used to balance the bilateral relationship between the local topology's optimisation and the global topology's maintenance. NSRGRN is compared with six state-of-the-art methods on three datasets (26 networks in total), and it shows the best all-round performance. Furthermore, when acting as a post-processing step, the NSR algorithm can improve the results of other methods in most datasets.

Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding-based Internet of Things (IoT) systems. To tackle this issue, we propose in this article a rapid detection approach, which consists of multistage stepwise discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The average detection precision (ADP), multiple box rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications.