A Comprehensive Survey on Deep Graph Representation Learning.

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future.

Carbomer Hydrogel Composed of Cu2O and Hematoporphyrin Monomethyl Ether Promotes the Healing of Infected Wounds.

Infectious wounds pose a significant challenge in the field of wound healing primarily due to persistent inflammation and the emergence of antibiotic-resistant bacteria. To combat these issues, the development of an effective wound dressing that can prevent infection and promote healing is of the utmost importance. Photodynamic therapy (PDT) has emerged as a promising noninvasive treatment strategy for tackling antibiotic-resistant bacteria. A biodegradable photosensitizer called hematoporphyrin monomethyl ether (HMME) has shown potential in generating reactive oxygen species (ROS) upon laser activation to combat bacteria. However, the insolubility of HMME limits its antibacterial efficacy and its ability to facilitate skin healing. To overcome these limitations, we have synthesized a compound hydrogel by combining carbomer, HMME, and Cu2O nanoparticles. This compound hydrogel exhibits enhanced antimicrobial ability and excellent biocompatibility and promotes angiogenesis, which is crucial for the healing of skin defects. By integrating the benefits of HMME, Cu2O nanoparticles, and the gel-forming properties of carbomer, this compound hydrogel shows great potential as an effective wound dressing material. In summary, the compound hydrogel developed in this study offers a promising solution for infectious wounds by addressing the challenges of infection prevention and promoting skin healing. This innovative approach utilizing PDT and the unique properties of the compound hydrogel could significantly improve the outcomes of wound healing in clinical settings.

Redundancy-Free Self-Supervised Relational Learning for Graph Clustering.

Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks (GNNs) in recent years. However, most existing methods overlook the inherent relational information among the nonindependent and nonidentically distributed nodes in a graph. Due to the lack of exploration of relational attributes, the semantic information of the graph-structured data fails to be fully exploited which leads to poor clustering performance. In this article, we propose a novel self-supervised deep graph clustering method named relational redundancy-free graph clustering (R 2 FGC) to tackle the problem. It extracts the attribute-and structure-level relational information from both global and local views based on an autoencoder (AE) and a graph AE (GAE). To obtain effective representations of the semantic information, we preserve the consistent relationship among augmented nodes, whereas the redundant relationship is further reduced for learning discriminative embeddings. In addition, a simple yet valid strategy is used to alleviate the oversmoothing issue. Extensive experiments are performed on widely used benchmark datasets to validate the superiority of our R 2 FGC over state-of-the-art baselines. Our codes are available at https://github.com/yisiyu95/R2FGC.

Few-shot Molecular Property Prediction via Hierarchically Structured Learning on Relation Graphs.

This paper studies few-shot molecular property prediction, which is a fundamental problem in cheminformatics and drug discovery. More recently, graph neural network based model has gradually become the theme of molecular property prediction. However, there is a natural deficiency for existing methods, that is, the scarcity of molecules with desired properties, which makes it hard to build an effective predictive model. In this paper, we propose a novel framework called Hierarchically Structured Learning on Relation Graphs (HSL-RG) for molecular property prediction, which explores the structural semantics of a molecule from both global-level and local-level granularities. Technically, we first leverage graph kernels to construct relation graphs to globally communicate molecular structural knowledge from neighboring molecules and then design self-supervised learning signals of structure optimization to locally learn transformation-invariant representations from molecules themselves. Moreover, we propose a task-adaptive meta-learning algorithm to provide meta knowledge customization for different tasks in few-shot scenarios. Experiments on multiple real-life benchmark datasets show that HSL-RG is superior to existing state-of-the-art approaches.

Promising therapeutic aspects in human genetic imprinting disorders.

Genomic imprinting is an epigenetic phenomenon of monoallelic gene expression pattern depending on parental origin. In humans, congenital imprinting disruptions resulting from genetic or epigenetic mechanisms can cause a group of diseases known as genetic imprinting disorders (IDs). Genetic IDs involve several distinct syndromes sharing homologies in terms of genetic etiologies and phenotypic features. However, the molecular pathogenesis of genetic IDs is complex and remains largely uncharacterized, resulting in a lack of effective therapeutic approaches for patients. In this review, we begin with an overview of the genomic and epigenomic molecular basis of human genetic IDs. Notably, we address ethical aspects as a priority of employing emerging techniques for therapeutic applications in human IDs. With a particular focus, we delineate the current field of emerging therapeutics for genetic IDs. We briefly summarize novel symptomatic drugs and highlight the key milestones of new techniques and therapeutic programs as they stand today which can offer highly promising disease-modifying interventions for genetic IDs accompanied by various challenges.

Arbutin-modified microspheres prevent osteoarthritis progression by mobilizing local anti-inflammatory and antioxidant responses.

Osteoarthritis (OA) is a common degenerative joint disease worldwide and currently there is no effective strategy to stop its progression. It is known that oxidative stress and inflammation can promote the development of OA, and therapeutic strategies against these conditions may alleviate OA. Arbutin (ARB), a major ingredient of the Chinese medicinal herb cowberry leaf, exerts good antioxidant and anti-inflammatory activities yet has not been studied in OA. Here we developed ARB-loaded gelatine methacryloyl-Liposome (GM-Lipo@ARB) microspheres which showed long-term release of ARB and excellent cartilage-targeting effects. The ARB-loaded microspheres effectively reduced the inflammatory response in interleukin (IL)-1ß-treated arthritic chondrocytes. Moreover, the synthesized GM-Lipo@ARB microspheres regulated cartilage extracellular matrix (ECM) homeostasis through anti-inflammation effect via inhibiting NF-κB signaling and anti-oxidative stress effect via activating Nrf2 pathway. Intra-articular use of GM-Lipo@ARB can effectively reduce inflammation and oxidative stress in the articular cartilage and thus, attenuating OA progression in a mouse model. The study proposed a novel ARB-laden functional microsphere, GM-Lipo@ARB, and demonstrated that this compound may be used as an alternative therapeutics for treating OA.

Dysregulated adipose tissue expansion and impaired adipogenesis in Prader-Willi syndrome children before obesity-onset.

OBJECTIVE: Prader-Willi syndrome (PWS) is a rare genetic imprinting disorder resulting from the expression loss of genes on the paternally inherited chromosome 15q11-13. Early-onset life-thriving obesity and hyperphagia represent the clinical hallmarks of PWS. The noncoding RNA gene SNORD116 within the minimal PWS genetic lesion plays a critical role in the pathogenesis of the syndrome. Despite advancements in understanding the genetic basis for PWS, the pathophysiology of obesity development in PWS remains largely uncharacterized. Here, we aimed to investigate the signatures of adipose tissue development and expansion pathways and associated adipose biology in PWS children without obesity-onset at an early stage, mainly from the perspective of the adipogenesis process, and further elucidate the underlying molecular mechanisms. METHODS: We collected inguinal (subcutaneous) white adipose tissues (ingWATs) from phase 1 PWS and healthy children with normal weight aged from 6 M to 2 Y. Adipose morphology and histological characteristics were assessed. Primary adipose stromal vascular fractions (SVFs) were isolated, cultured in vitro, and used to determine the capacity and function of white and beige adipogenic differentiation. High-throughput RNA-sequencing (RNA-seq) was performed in adipose-derived mesenchymal stem cells (AdMSCs) to analyze transcriptome signatures in PWS subjects. Transient repression of SNORD116 was conducted to evaluate its functional relevance in adipogenesis. The changes in alternative pre-mRNA splicing were investigated in PWS and SNORD116 deficient cells. RESULTS: In phase 1 PWS children, impaired white adipose tissue (WAT) development and unusual fat expansion occurred long before obesity onset, which was characterized by the massive enlargement of adipocytes accompanied by increased apoptosis. White and beige adipogenesis programs were impaired and differentiated adipocyte functions were disturbed in PWS-derived SVFs, despite increased proliferation capacity, which were consistent with the results of RNA-seq analysis of PWS AdMSCs. We also experimentally validated disrupted beige adipogenesis in adipocytes with transient SNORD116 downregulation. The transcript and protein levels of PPARγ, the adipogenesis master regulator, were significantly lower in PWS than in control AdMSCs as well as in SNORD116 deficient AdMSCs/adipocytes than in scramble (Scr) cells, resulting in the inhibited adipogenic program. Additionally, through RNA-seq, we observed aberrant transcriptome-wide alterations in alternative RNA splicing patterns in PWS cells mediated by SNORD116 loss and specifically identified a changed PRDM16 gene splicing profile in vitro. CONCLUSIONS: Imbalance in the WAT expansion pathway and developmental disruption are primary defects in PWS displaying aberrant adipocyte hypertrophy and impaired adipogenesis process, in which SNORD116 deficiency plays a part. Our findings suggest that dysregulated adiposity specificity existing at an early phase is a potential pathological mechanism exacerbating hyperphagic obesity onset in PWS. This mechanistic evidence on adipose biology in young PWS patients expands knowledge regarding the pathogenesis of PWS obesity and may aid in developing a new therapeutic strategy targeting disturbed adipogenesis and driving AT plasticity to combat abnormal adiposity and associated metabolic disorders for PWS patients.

Evaluation of One Belt One Road publications: a bibliometric and literature review analysis.

AbstractThe aim of the "One Belt and One Road" (OBOR) project proposed by China is to encourage extension of global value chains, facilitate regional integration and increase efficient resource allocation. In recent times, scientific literature has examined the investment decisions of the OBOR investment and how it will affect the governmental policies, environmental initiatives, and bilateral flow of economic resources. Current study, based on Web of Science database, uses bibliometric methodology to map the research trends in OBOR publications. We contribute in the economic literature in the associated fields of OBOR publications in the following ways: (1) identify the most influential researchers, articles, and academic institutions, (2) mapping the interdisciplinary character of OBOR investments and its bibliometric similarity to adjacent fields, (3) visualize nature and trends of the research field, and (4) synthesizing future research areas. Although OBOR initiative has received considerable traction, but to this date, there is no bibliometric study on this topic. The findings of current study will help policymakers and academics to navigate the OBOR literature, provide a systematic basis for developing the field, and suggest promising future research avenues.

Regulatory roles and mechanisms of alternative RNA splicing in adipogenesis and human metabolic health.

Alternative splicing (AS) regulates gene expression patterns at the post-transcriptional level and generates a striking expansion of coding capacities of genomes and cellular protein diversity. RNA splicing could undergo modulation and close interaction with genetic and epigenetic machinery. Notably, during the adipogenesis processes of white, brown and beige adipocytes, AS tightly interplays with the differentiation gene program networks. Here, we integrate the available findings on specific splicing events and distinct functions of different splicing regulators as examples to highlight the directive biological contribution of AS mechanism in adipogenesis and adipocyte biology. Furthermore, accumulating evidence has suggested that mutations and/or altered expression in splicing regulators and aberrant splicing alterations in the obesity-associated genes are often linked to humans' diet-induced obesity and metabolic dysregulation phenotypes. Therefore, significant attempts have been finally made to overview novel detailed discussion on the prospects of splicing machinery with obesity and metabolic disorders to supply featured potential management mechanisms in clinical applicability for obesity treatment strategies.