Single-Cell Multimodal Prediction via Transformers

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics. Nevertheless, the proliferation of multimodal single-cell data also introduces tremendous challenges in modeling the complex interactions among different modalities. The recently advanced methods focus on constructing static interaction graphs and applying graph neural networks (GNNs) to learn from multimodal data. However, such static graphs can be suboptimal as they do not take advantage of the downstream task information; meanwhile GNNs also have some inherent limitations when deeply stacking GNN layers. To tackle these issues, in this work, we investigate how to leverage transformers for multimodal single-cell data in an end-to-end manner while exploiting downstream task information. In particular, we propose a scMoFormer framework which can readily incorporate external domain knowledge and model the interactions within each modality and cross modalities. Extensive experiments demonstrate that scMoFormer achieves superior performance on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022 competition. Our implementation is publicly available at Github.Comment: CIKM 202

Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi

Effect of different stunning methods on antioxidant status, myofibrillar protein oxidation, and gelation properties of large yellow croaker during postmortem

Post-mortem muscle biochemical processes play a crucial role on fish fillets quality and they are strictly linked to stunning methods. The improper stunning methods before slaughter could cause the fish to deteriorate more quickly during cold storage. This study aimed to investigate the effect of stunning methods (hit on the head, T1; gill cut, T2; immersion in ice/water slurry, T3; CO2 narcosis, T4; 40% CO2 + 30 % N2 + 30% O2, T5) on myofibrillar proteins (MPs) of large yellow croaker. The results indicated that T2 and T3 samples were significantly damaged compared with other samples, which reflected that the activities of total superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) were significantly damaged during cold storage in T2 and T3 samples. And the gill cut and immersion in ice/water slurry resulted in the generation of protein carbonyl, the decrease of Ca2+-ATPase, free ammonia and protein solubility, and the production of dityrosine during storage. In addition, MPs gel of T2 and T3 samples showed the decrease of water hold capacity (WHC) and whiteness, structure destruction, and water migration. The T4 samples had the least damage of MPs and gel structure during cold storage

Immunomodulatory Properties of Stem Cells in Periodontitis: Current Status and Future Prospective

Periodontitis is the sixth-most prevalent chronic inflammatory disease and gradually devastates tooth-supporting tissue. The complexity of periodontal tissue and the local inflammatory microenvironment poses great challenges to tissue repair. Recently, stem cells have been considered a promising strategy to treat tissue damage and inflammation because of their remarkable properties, including stemness, proliferation, migration, multilineage differentiation, and immunomodulation. Several varieties of stem cells can potentially be applied to periodontal regeneration, including dental mesenchymal stem cells (DMSCs), nonodontogenic stem cells, and induced pluripotent stem cells (iPSCs). In particular, these stem cells possess extensive immunoregulatory capacities. In periodontitis, these cells can exert anti-inflammatory effects and regenerate the periodontium. Stem cells derived from infected tissue possess typical stem cell characteristics with lower immunogenicity and immunosuppression. Several studies have demonstrated that these cells can also regenerate the periodontium. Furthermore, the interaction of stem cells with the surrounding infected microenvironment is critical to periodontal tissue repair. Though the immunomodulatory capabilities of stem cells are not entirely clarified, they show promise for therapeutic application in periodontitis. Here, we summarize the potential of stem cells for periodontium regeneration in periodontitis and focus on their characteristics and immunomodulatory properties as well as challenges and perspectives

Immunomodulatory Properties of Stem Cells in Periodontitis: Current Status and Future Prospective

Periodontitis is the sixth-most prevalent chronic inflammatory disease and gradually devastates tooth-supporting tissue. The complexity of periodontal tissue and the local inflammatory microenvironment poses great challenges to tissue repair. Recently, stem cells have been considered a promising strategy to treat tissue damage and inflammation because of their remarkable properties, including stemness, proliferation, migration, multilineage differentiation, and immunomodulation. Several varieties of stem cells can potentially be applied to periodontal regeneration, including dental mesenchymal stem cells (DMSCs), nonodontogenic stem cells, and induced pluripotent stem cells (iPSCs). In particular, these stem cells possess extensive immunoregulatory capacities. In periodontitis, these cells can exert anti-inflammatory effects and regenerate the periodontium. Stem cells derived from infected tissue possess typical stem cell characteristics with lower immunogenicity and immunosuppression. Several studies have demonstrated that these cells can also regenerate the periodontium. Furthermore, the interaction of stem cells with the surrounding infected microenvironment is critical to periodontal tissue repair. Though the immunomodulatory capabilities of stem cells are not entirely clarified, they show promise for therapeutic application in periodontitis. Here, we summarize the potential of stem cells for periodontium regeneration in periodontitis and focus on their characteristics and immunomodulatory properties as well as challenges and perspectives

S, N Co-Doped Graphene Quantum Dot/TiO2 Composites for Efficient Photocatalytic Hydrogen Generation

Abstract S, N co-doped graphene quantum dots (S,N-GQDs) coupled with P25 (TiO2) (S,N-GQD/P25) have been prepared via simply hydrothermal method. The as-prepared S,N-GQD/P25 composites exhibited excellent photocatalytic hydrogen generation activities, with a significantly extended light absorption range and superior durability without loading any noble metal cocatalyst. The photocatalytic activity of this composite under visible light (λ = 400–800 nm) was greatly improved compared with that of pure P25. This remarkable improvement in photocatalytic activity of the S,N-GQD/P25 composites can be attributed to that S,N-GQDs play a key role to enhance visible light absorption and facilitate the separation and transfer of photogenerated electrons and holes. Generally, this work could provide new insights into the facile fabrication of photocatalytic composites as high performance photocatalysts

A Preliminary Study on the Effects of Nitrite Exposure on Hematological Parameters, Oxidative Stress, and Immune-Related Responses in Pearl Gentian Grouper

Nitrite represents one of the most typical contaminants in aqueous species. The research was conducted to evaluate the impacts of nitrite exposure on the survival, gill morphology, hematological parameters, immune response, and meat flavor of pearl gentian grouper. The fish were exposed to 0, 5, 10, and 20 mg/L of nitrite for 96 h (note: N-0, N-5, N-10, and N-20 indicate nitrite concentrations of 0, 5, 10, and 20 mg/L, respectively). The blood, gills, and muscles were collected from fish to determine hematological parameters, immune response, oxidative stress, and meat flavor after 0, 12, 24, 36, 48, 60, 72, and 96 h of exposure. The data showed that the aspartate aminotransferase (AST), cortisol (COR), malondialdehyde (MDA), alanine aminotransferase (ALT), alkaline phosphatase (AKP), and free amino acids (FAAs) contents were significantly increased, while the glutathione (GSH), immunoglobulin M (IgM), superoxide dismutase (SOD), and lysozyme (LZM) contents were remarkably declined in the N-20 group after 72 h of exposure. In gills, exposure to the higher concentrations of nitrite resulted in the proliferation and hypertrophy of epithelial cells of gill lamellae, as well as an increase in mucous cells. In addition, all fish in the N-10 and N-20 groups died after 96 h of exposure. Our findings suggested that exposure to higher concentrations of nitrite disrupted blood physiology and oxidative stress, leading to dysfunction in the pearl gentian grouper