Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions

Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit correlations present between drug pairs (DPs), which leads to weak predictions. To address this issue, this paper introduces a hierarchical multi-relational graph representation learning (HMGRL) approach. Within the framework of HMGRL, we leverage a wealth of drug-related heterogeneous data sources to construct heterogeneous graphs, where nodes represent drugs and edges denote clear and various associations. The relational graph convolutional network (RGCN) is employed to capture diverse explicit relationships between drugs from these heterogeneous graphs. Additionally, a multi-view differentiable spectral clustering (MVDSC) module is developed to capture multiple valuable implicit correlations between DPs. Within the MVDSC, we utilize multiple DP features to construct graphs, where nodes represent DPs and edges denote different implicit correlations. Subsequently, multiple DP representations are generated through graph cutting, each emphasizing distinct implicit correlations. The graph-cutting strategy enables our HMGRL to identify strongly connected communities of graphs, thereby reducing the fusion of irrelevant features. By combining every representation view of a DP, we create high-level DP representations for predicting DDIs. Two genuine datasets spanning three distinct tasks are adopted to gauge the efficacy of our HMGRL. Experimental outcomes unequivocally indicate that HMGRL surpasses several leading-edge methods in performance.Comment: 14 pages,10 figure

A critical review of resistance and oxidation mechanisms of Sb-oxidizing bacteria for the bioremediation of Sb(Ⅲ) pollution

Antimony (Sb) is a priority pollutant in many countries and regions due to its chronic toxicity and potential carcinogenicity. Elevated concentrations of Sb in the environmental originating from mining and other anthropogenic sources are of particular global concern, so the prevention and control of the source of pollution and environment remediation are urgent. It is widely accepted that indigenous microbes play an important role in Sb speciation, mobility, bioavailability, and fate in the natural environment. Especially, antimony-oxidizing bacteria can promote the release of antimony from ore deposits to the wider environment. However, it can also oxidize the more toxic antimonite [Sb(III)] to the less-toxic antimonate [Sb(V)], which is considered as a potentially environmentally friendly and efficient remediation technology for Sb pollution. Therefore, understanding its biological oxidation mechanism has great practical significance to protect environment and human health. This paper reviews studies of the isolation, identification, diversity, Sb(III) resistance mechanisms, Sb(III) oxidation characteristics and mechanism and potential application of Sb-oxidizing bacteria. The aim is to provide a theoretical basis and reference for the diversity and metabolic mechanism of Sb-oxidizing bacteria, the prevention and control of Sb pollution sources, and the application of environment treatment for Sb pollution

Immunocytochemical localization of neuropeptide Y,serotonin,substance P and β-endorphin in optic ganglia and brain of Metapenaeus ensis

【英文摘要】 By using immunocytochemistry method of Strept Avidin-Biotin-Complex, four kinds of antisera raised against rabbits were applied to observe the immunoreactive neurons and neuropils of sero-tonin (5-HT), neuropeptide Y (NPY), substance P (SP) and β-Endorphin (β-Ep) in optic ganglia and brain of Metapenaeus ensis. The results showed that, the 5-HT-immunoreactive cells were located in all the four neuropils of optic ganglia. Immunoreactivity of 5-HT was detected in anterior medial protocerebrum neuropils (AMPN...Supported by the Key Foundation Research Program of Fujian Province (1998-2002

Relation-aware subgraph embedding with co-contrastive learning for drug-drug interaction prediction

Relation-aware subgraph embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-relational DDI graph and then learning relation-aware subgraph embeddings (RaSEs) of drugs from the DDI graph. However, most existing approaches are usually limited in learning RaSEs of new drugs, leading to serious over-fitting when the test DDIs involve such drugs. To alleviate this issue, We propose a novel DDI prediction method based on relation-aware subgraph embedding with co-contrastive learning, RaSECo. RaSECo constructs two heterogeneous drug graphs: a multi-relational DDI graph and a multi-attributes-based drug-drug similarity (DDS) graph. The two graphs are used respectively for learning and propagating the RaSEs of drugs, thereby ensuring that all drugs, including new ones, can aggregate effective RaSEs. Additionally, we employ a cross-view contrastive mechanism to enhance drug-pair (DP) embedding. RaSECo learns DP embeddings from two distinct views (interaction and similarity views) and encourages these views to supervise each other collaboratively to obtain more discriminative DP embeddings. We evaluate the effectiveness of our RaSECo on three different tasks using two real datasets. The experimental results demonstrate that RaSECo outperforms existing state-of-the-art prediction methods.Comment: 14pages, 23figure

The Ponto-Geniculo-Occipital (PGO) Waves in Dreaming: An Overview

Rapid eye movement (REM) sleep is the main sleep correlate of dreaming. Ponto-geniculo-occipital (PGO) waves are a signature of REM sleep. They represent the physiological mechanism of REM sleep that specifically limits the processing of external information. PGO waves look just like a message sent from the pons to the lateral geniculate nucleus of the visual thalamus, the occipital cortex, and other areas of the brain. The dedicated visual pathway of PGO waves can be interpreted by the brain as visual information, leading to the visual hallucinosis of dreams. PGO waves are considered to be both a reflection of REM sleep brain activity and causal to dreams due to their stimulation of the cortex. In this review, we summarize the role of PGO waves in potential neural circuits of two major theories, i.e., (1) dreams are generated by the activation of neural activity in the brainstem; (2) PGO waves signaling to the cortex. In addition, the potential physiological functions during REM sleep dreams, such as memory consolidation, unlearning, and brain development and plasticity and mood regulation, are discussed. It is hoped that our review will support and encourage research into the phenomenon of human PGO waves and their possible functions in dreaming