A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data.

In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.

msBERT-Promoter: a multi-scale ensemble predictor based on BERT pre-trained model for the two-stage prediction of DNA promoters and their strengths.

BACKGROUND: A promoter is a specific sequence in DNA that has transcriptional regulatory functions, playing a role in initiating gene expression. Identifying promoters and their strengths can provide valuable information related to human diseases. In recent years, computational methods have gained prominence as an effective means for identifying promoter, offering a more efficient alternative to labor-intensive biological approaches. RESULTS: In this study, a two-stage integrated predictor called "msBERT-Promoter" is proposed for identifying promoters and predicting their strengths. The model incorporates multi-scale sequence information through a tokenization strategy and fine-tunes the DNABERT model. Soft voting is then used to fuse the multi-scale information, effectively addressing the issue of insufficient DNA sequence information extraction in traditional models. To the best of our knowledge, this is the first time an integrated approach has been used in the DNABERT model for promoter identification and strength prediction. Our model achieves accuracy rates of 96.2% for promoter identification and 79.8% for promoter strength prediction, significantly outperforming existing methods. Furthermore, through attention mechanism analysis, we demonstrate that our model can effectively combine local and global sequence information, enhancing its interpretability. CONCLUSIONS: msBERT-Promoter provides an effective tool that successfully captures sequence-related attributes of DNA promoters and can accurately identify promoters and predict their strengths. This work paves a new path for the application of artificial intelligence in traditional biology.

PSAC-6mA: 6mA site identifier using self-attention capsule network based on sequence-positioning.

DNA N6-methyladenine (6mA) modifications play a pivotal role in the regulation of growth, development, and diseases in organisms. As a significant epigenetic marker, 6mA modifications extensively participate in the intricate regulatory networks of the genome. Hence, gaining a profound understanding of how 6mA is intricately involved in these biological processes is imperative for deciphering the gene regulatory networks within organisms. In this study, we propose PSAC-6mA (Position-self-attention Capsule-6mA), a sequence-location-based self-attention capsule network. The positional layer in the model enables positional relationship extraction and independent parameter setting for each base position, avoiding parameter sharing inherent in convolutional approaches. Simultaneously, the self-attention capsule network enhances dimensionality, capturing correlation information between capsules and achieving exceptional results in feature extraction across multiple spatial dimensions within the model. Experimental results demonstrate the superior performance of PSAC-6mA in recognizing 6mA motifs across various species.

AGF-PPIS: A protein-protein interaction site predictor based on an attention mechanism and graph convolutional networks.

Protein-protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein-protein interactions sites is crucial. In this paper, we propose a novel predictor for protein-protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https://github.com/fxh1001/AGF-PPIS.

Machine learning-based prediction model for distant metastasis of breast cancer.

BACKGROUND: Breast cancer is the most prevalent malignancy in women. Advanced breast cancer can develop distant metastases, posing a severe threat to the life of patients. Because the clinical warning signs of distant metastasis are manifested in the late stage of the disease, there is a need for better methods of predicting metastasis. METHODS: First, we screened breast cancer distant metastasis target genes by performing difference analysis and weighted gene co-expression network analysis (WGCNA) on the selected datasets, and performed analyses such as GO enrichment analysis on these target genes. Secondly, we screened breast cancer distant metastasis target genes by LASSO regression analysis and performed correlation analysis and other analyses on these biomarkers. Finally, we constructed several breast cancer distant metastasis prediction models based on Logistic Regression (LR) model, Random Forest (RF) model, Support Vector Machine (SVM) model, Gradient Boosting Decision Tree (GBDT) model and eXtreme Gradient Boosting (XGBoost) model, and selected the optimal model from them. RESULTS: Several 21-gene breast cancer distant metastasis prediction models were constructed, with the best performance of the model constructed based on the random forest model. This model accurately predicted the emergence of distant metastases from breast cancer, with an accuracy of 93.6 %, an F1-score of 88.9 % and an AUC value of 91.3 % on the validation set. CONCLUSION: Our findings have the potential to be translated into a point-of-care prognostic analysis to reduce breast cancer mortality.

Fatigue, sleep and physical activity in postoperative patients with pituitary adenomas: A survey.

BACKGROUND: The condition and correlation of fatigue, sleep and physical activity in postoperative patients with pituitary adenomas remain unclear. This survey aimed to evaluate the current status and influencing factors of fatigue, sleep and physical activity in postoperative patients with pituitary adenomas. METHODS: Patients undergoing pituitary adenoma resection in two tertiary hospitals from November 2019 to November 2021 were included. The general data questionnaire, Multidimensional Fatigue Inventory (MFI-20), Pittsburgh Sleep Quality Index (PSQI) and international physical activity questionnaire were used for data analysis. RESULTS: In total, 184 patients with pituitary adenomas were included. The postoperative patients with pituitary adenomas had a high level of fatigue. In total, 34 (18.5%) patients had low level of physical activity, 76(41.3%) patients had medium level of physical activity and 74 (40.2%) had high level of physical activity. Postoperative time, PSQI, physical activity level and gender were the influencing factors of fatigue in patients with pituitary adenomas (all P < 0.05). CONCLUSIONS: Postoperative patients with pituitary adenomas have a higher level of fatigue, and it is related to reduced sleep quality and activity. Relevant nursing measures should be taken according to the influencing factors of fatigue to reduce the fatigue of postoperative patients with pituitary adenomas.

Integration tools for scRNA-seq data and spatial transcriptomics sequencing data.

Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries. However, each method has its own advantages, limitations and scope of application. Researchers need to select the most suitable method for their research purposes based on the actual situation. This review article presents a compilation of 19 integration methods sourced from a wide range of available approaches, serving as a comprehensive reference for researchers to select the suitable integration method for their specific research inquiries. By understanding the principles of these methods, we can identify their similarities and differences, comprehend their applicability and potential complementarity, and lay the foundation for future method development and understanding. This review article presents 19 methods that aim to integrate scRNA-seq data and spatial transcriptomics data. The methods are classified into two main groups and described accordingly. The article also emphasizes the incorporation of High Variance Genes in annotating various technologies, aiming to obtain biologically relevant information aligned with the intended purpose.

RAVAR: a curated repository for rare variant-trait associations.

Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.

Stress overload, influencing factors, and psychological experiences of nurse managers during early stages of the COVID-19 pandemic: a sequential explanatory mixed method study.

Background: Healthcare systems had an exceptionally difficult time during the early COVID-19 pandemic. Nurse managers in particular made enormous contributions to ensuring the safety of patients and front-line nurses while being under excessive psychological stress. However, little is known about their experiences during this time. Objective: The aim of this study was thus to assess the level of stress overload and psychological feelings of nurse managers during the early COVID-19 pandemic. Methods: A mixed methods sequential explanatory design study with non-random convenience sampling was performed, following the STROBE and COREQ checklists. The study was conducted at the Affiliated Dongyang Hospital, Wenzhou Medical University, with data collected from six provinces in southern China (Zhejiang, Hubei, Shanghai, Jiangsu, Hunan and Jiangxi) during March 2020 and June 2020. A total of 966 nurse managers completed the Stress Overload Scale and Work-Family Support Scale. In addition, a nested sample of nurse managers participated in semi-structured face-to-face interviews. The data were then analyzed using qualitative content analysis, Pearson correlation, and multiple linear regression. Results: The quantitative results showed that nurse managers experienced a moderate level of stress load. There was a significant negative correlation between work-family support and stress load (r = -0.551, p < 0.01). Concerns about protecting front-line nurses and work-family support were the main factors affecting the stress load, which accounted for 34.0% of the total variation. Qualitative analysis identified four main thematic analyses that explained stress load: (1) great responsibility and great stress, (2) unprecedented stress-induced stress response, (3) invisible stress: the unknown was even more frightening, and (4) stress relief from love and support. Taken together these findings indicate that concern about protecting front-line nurses and negative work-family support of nurse managers were the main factors causing stress overload. Conclusion: Implementing measures focused on individual psychological adjustment combined with community and family support and belongingness is one potential strategy to reduce psychological stress among nurse managers.

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting.

An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions. Electronic Supplementary material: Supplementary material is available in the online version of this article at 10.1007/s11783-023-1677-1 and is accessible for authorized users.

Behavioral Intentions and Factors Influencing Nurses' Care of COVID-19 Patients: A Cross-Sectional Study.

Objective: Behavioral intentions to care for patients with infectious diseases are crucial for improving quality of care. However, there have been few studies of the behavioral intentions and factors influencing patient care by clinical nurses during the COVID-19 pandemic. This study aims to explore cognition, attitudes, subjective norms, self-efficacy, and behavioral intentions of clinical nurses while caring for COVID-19 patients and to explore any influencing factors. Method: A cross-sectional survey was conducted of nurses through convenience sampling in southeast China from February 2020 to March 2020. The questionnaire was developed based on the theory of planned behavior and self-efficacy. Results: A total of 774 nurses completed the survey. Of these, 69.12% (535/774) reported positive behavioral intentions, 75.58% (585/774) reported a positive attitude, and 63.82% (494/774) reported having the confidence to care for patients. However, the lack of support from family and friends and special allowance affected their self-confidence. Attitude, self-efficacy, subjective norms, and ethical cognition were significantly positively correlated with behavioral intentions (r = 0.719, 0.690, 0.603, and 0.546, respectively, all P < 0.001). Structural equation model showed that self-efficacy, attitude, ethical cognition, and subjective norms had positive effects on behavioral intentions (ß = 0.402, 0.382, 0.091, and 0.066, respectively, P < 0.01). The total effect of behavioral intentions was influenced by attitude, ethical cognition, self-efficacy, and subjective norms (ß = 0.656, 0.630, 0.402, and 0.157, respectively, P < 0.01). In addition, ethical cognition had a positive mediating effect on behavioral intentions (ß = 0.539, P < 0.001). Conclusion: The study results indicated that attitude, ethical cognition, and self-efficacy were the main factors influencing nurses' behavioral intention. Efforts should be made to improve nurses' attitude and self-efficacy through ethical education and training to increase behavioral intentions to care for patients with infectious diseases, which will improve the quality of nursing care.

DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins.

Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play vital roles in gene expression. Accurate identification of these proteins is crucial. However, there are two existing challenges: one is the problem of ignoring DNA- and RNA-binding proteins (DRBPs), and the other is a cross-predicting problem referring to DBP predictors predicting DBPs as RBPs, and vice versa. In this study, we proposed a computational predictor, called DeepMC-iNABP, with the goal of solving these difficulties by utilizing a multiclass classification strategy and deep learning approaches. DBPs, RBPs, DRBPs and non-NABPs as separate classes of data were used for training the DeepMC-iNABP model. The results on test data collected in this study and two independent test datasets showed that DeepMC-iNABP has a strong advantage in identifying the DRBPs and has the ability to alleviate the cross-prediction problem to a certain extent. The web-server of DeepMC-iNABP is freely available at http://www.deepmc-inabp.net/. The datasets used in this research can also be downloaded from the website.

webSCST: an interactive web application for single-cell RNA-sequencing data and spatial transcriptomic data integration.

SUMMARY: Integrative analysis of single-cell RNA-sequencing (scRNA-seq) data with spatial data for the same species and organ would provide each cell sample with a predictive spatial location, which would facilitate biological study. However, publicly available spatial sequencing datasets for specific species and organs are rare and are often displayed in different formats. In this study, we introduce a new web-based scRNA-seq analysis tool, webSCST, that integrates well-organized spatial transcriptome sequencing datasets categorized by species and organs, provides a user-friendly interface for raw single-cell processing with popular integration methods and allows users to submit their raw scRNA-seq data once to obtain predicted spatial locations for each cell type. AVAILABILITY AND IMPLEMENTATION: webSCST implemented in shiny with all major browsers supported is available at http://www.webscst.com. webSCST is also freely available as an R package at https://github.com/swsoyee/webSCST.

Protein-DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data.

With the development of artificial intelligence (AI) technologies and the availability of large amounts of biological data, computational methods for proteomics have undergone a developmental process from traditional machine learning to deep learning. This review focuses on computational approaches and tools for the prediction of protein-DNA/RNA interactions using machine intelligence techniques. We provide an overview of the development progress of computational methods and summarize the advantages and shortcomings of these methods. We further compiled applications in tasks related to the protein-DNA/RNA interactions, and pointed out possible future application trends. Moreover, biological sequence-digitizing representation strategies used in different types of computational methods are also summarized and discussed.

webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study.

The development of transcriptome-wide association studies (TWAS) has enabled researchers to better identify and interpret causal genes in many diseases. However, there are currently no resources providing a comprehensive listing of gene-disease associations discovered by TWAS from published GWAS summary statistics. TWAS analyses are also difficult to conduct due to the complexity of TWAS software pipelines. To address these issues, we introduce a new resource called webTWAS, which integrates a database of the most comprehensive disease GWAS datasets currently available with credible sets of potential causal genes identified by multiple TWAS software packages. Specifically, a total of 235 064 gene-diseases associations for a wide range of human diseases are prioritized from 1298 high-quality downloadable European GWAS summary statistics. Associations are calculated with seven different statistical models based on three popular and representative TWAS software packages. Users can explore associations at the gene or disease level, and easily search for related studies or diseases using the MeSH disease tree. Since the effects of diseases are highly tissue-specific, webTWAS applies tissue-specific enrichment analysis to identify significant tissues. A user-friendly web server is also available to run custom TWAS analyses on user-provided GWAS summary statistics data. webTWAS is freely available at http://www.webtwas.net.

Single-cell RNA analysis reveals the potential risk of organ-specific cell types vulnerable to SARS-CoV-2 infections.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease 2019 (COVID-19) since December 2019 that has led to more than 160 million confirmed cases, including 3.3 million deaths. To understand the mechanism by which SARS-CoV-2 invades human cells and reveal organ-specific susceptible cell types for COVID-19, we conducted comprehensive bioinformatic analysis using public single-cell RNA sequencing datasets. Utilizing the expression information of six confirmed COVID-19 receptors (ACE2, TMPRSS2, NRP1, AXL, FURIN and CTSL), we demonstrated that macrophages are the most likely cells that may be associated with SARS-CoV-2 pathogenesis in lung. Besides the widely reported 'chemokine storm', we identified ribosome related pathways that may also be potential therapeutic target for COVID-19 lung infection patients. Moreover, cell-cell communication analysis and trajectory analysis revealed that M1-like macrophages showed the highest relation to severe COVID-19 patients. And we also demonstrated that up-regulation of chemokine pathways generally lead to severe symptoms, while down-regulation of ribosome and RNA activity related pathways are more likely to be mild. Other organ-specific susceptible cell type analyses could also provide potential targets for COVID-19 therapy. This work can provide clues for understanding the pathogenesis of COVID-19 and contribute to understanding the mechanism by which SARS-CoV-2 invades human cells.

Critical downstream analysis steps for single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

Single-cell RNA Sequencing Analysis Identifies Key Genes in Brain Metastasis from Lung Adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LADC) is the most common type of lung cancer and is a subtype of non-small-cell lung cancer (NSCLC). Approximately 40% of LADC patients experience brain metastases (BMs) during the course of the disease. In this study, integrated bioinformatics methods were applied to identify key genes related to brain metastasis in lung adenocarcinoma. METHODS: We derived and characterized genes differentially expressed between the primary tumour and brain metastases using tumour cells isolated from two lung cancer Patient-derived xenografts (PDX) cases (GSE 69405). Gene ontology (GO) and KEGG pathway enrichment analyses were applied, and protein-protein interaction (PPI) networks and Cytoscape software were utilized to identify key genes. RESULTS: Four key genes, including CKAP4 (Cytoskeleton Associated Protein 4), SERPINA1 (Serpin Family A Member 1), SDC2 (Syndecan 2) and GNG11 (G Protein Subunit Gamma 11) were identified for BM-LADC by the Venn diagram. CONCLUSION: We believe these key genes may be potential biomarkers for improved prognosis and treatment of lung adenocarcinoma.

Bronchopulmonary Dysplasia and Pulmonary Hypertension. The Role of Smooth Muscle adh5.

Bronchopulmonary dysplasia (BPD) is characterized by alveolar simplification, airway hyperreactivity, and pulmonary hypertension. In our BPD model, we have investigated the metabolism of the bronchodilator and pulmonary vasodilator GSNO (S-nitrosoglutathione). We have shown the GSNO catabolic enzyme encoded by adh5 (alcohol dehydrogenase-5), GSNO reductase, is epigenetically upregulated in hyperoxia. Here, we investigated the distribution of GSNO reductase expression in human BPD and created an animal model that recapitulates the human data. Blinded comparisons of GSNO reductase protein expression were performed in human lung tissues from infants and children with and without BPD. BPD phenotypes were evaluated in global (adh5-/-) and conditional smooth muscle (smooth muscle/adh5-/-) adh5 knockout mice. GSNO reductase was prominently expressed in the airways and vessels of human BPD subjects. Compared with controls, expression was greater in BPD smooth muscle, particularly in vascular smooth muscle (2.4-fold; P = 0.003). The BPD mouse model of neonatal hyperoxia caused significant alveolar simplification, airway hyperreactivity, and right ventricular and vessel hypertrophy. Global adh5-/- mice were protected from all three aspects of BPD, whereas smooth muscle/adh5-/- mice were only protected from pulmonary hypertensive changes. These data suggest adh5 is required for the development of BPD. Expression in the pulmonary vasculature is relevant to the pathophysiology of BPD-associated pulmonary hypertension. GSNO-mimetic agents or GSNO reductase inhibitors, both of which are currently in clinical trials for other conditions, could be considered for further study in BPD.