Abstract Background Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle ...to provide timely and accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency. Methods We propose CDSKNN XMBD (Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure. Results We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods. Conclusions The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.
Change in the composition of intestinal microbiota is associated with metabolic disorders such as gestational diabetes mellitus (GDM).
To understand how the microbiota impacts the development of ...gestational diabetes mellitus, we profiled the intestinal microbiome of 54 pregnant women, including 27 GDM subjects, by employing 16S rRNA gene sequencing. Additionally, we conducted targeted metabolomics assays to validate the identified pathways with overrepresented metabolites.
We evaluated the patterns of changing abundances of operational taxonomic units (OTU) between GDM and the healthy counterparts over three timepoints. Based on the significant OTUs, we inferred 132 significantly altered metabolic pathways in GDM. And identified two overrepresented metabolites of pregnancy hormone, butyrate and mevalonate, as potential intermediary metabolites of intestinal microbiota in GDM. Finally, we validated the impacts of the intestinal microbiota on GDM by demonstrating consistent changes of the serum levels of progesterone, estradiol, butyrate, and mevalonate in an independent cohort.
Our findings confirm that alterations in the microbiota play a role in the development of GDM by impacting the metabolism of pregnancy hormones. This provides a novel perspective on the pathogenesis of GDM and introduces potential biomarkers that can be used for early diagnosis and prevention of the disease.
In this study, the impact of prenatal exposure to Epigallocatechin gallate (EGCG) on the liver of adult offspring mice was investigated. While EGCG is known for its health benefits, its effects of ...prenatal exposure on the liver remain unclear. Pregnant C57BL/6 J mice were exposed to 1 mg/kg of EGCG for 16 days to assess hepatotoxicity effects of adult offspring. Transcriptomics and metabolomics were employed to elucidate the hepatotoxicity mechanisms. The findings revealed that prenatal EGCG exposure led to a decrease in liver somatic index, enhanced inflammatory responses and disrupted liver function through increased glycogen accumulation in adult mice. The integrated omics analysis revealed significant alterations in key pathways involved in liver glucose lipid metabolism, such as gluconeogenesis, dysregulation of insulin signaling, and induction of liver inflammation. Furthermore, the study found a negative correlation between the promoter methylation levels of Ppara and their mRNA levels, suggesting that EGCG could reduce hepatic lipid content through epigenetic modifications. The findings suggest that prenatal EGCG exposure can have detrimental impacts on the liver among adult individuals and emphasize the need for a comprehensive evaluation of the potential risks associated with EGCG consumption during pregnancy.
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•We propose a model-based downsampling algorithm for single-cell RNA sequencing analysis, the minimal unbiased representative points (MURPXMBD).•MURP defines a set of representative ...points by reducing gene-wise random, independent errors, while best retaining the covariance structure of biological origin.•MURP provides unbiased representation of the original cell population, which is robust to highly imbalanced representation of cell types and batch effects, thus improve the clustering proficiency and downstream integration analysis.
The random noises, sampling biases, and batch effects often confound true biological variations in single-cell RNA-sequencing (scRNA-seq) data. Adjusting such biases is key to the robust discoveries in downstream analyses, such as cell clustering, gene selection and data integration. Here we propose a model-based downsampling algorithm based on minimal unbiased representative points (MURPXMBD). MURPXMBD is designed to retrieve a set of representative points by reducing gene-wise random independent errors, while retaining the covariance structure of biological origin hence provide an unbiased representation of the cell population. Subsequent validation using benchmark datasets shows that MURPXMBD can improve the quality and accuracy of clustering algorithms, and thus facilitate the discovery of new cell types. Besides, MURPXMBD also improves the performance of dataset integration algorithms. In summary, MURPXMBD serves as a useful noise-reduction method for single-cell sequencing analysis in biomedical studies.
Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and ...accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency.
We propose CDSKNN
(Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure.
We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods.
The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.
In this paper, our main aim is to investigate the strong convergence rate of the truncated Euler-Maruyama approximations for stochastic differential equations with superlinearly growing drift ...coefficients. When the diffusion coefficient is polynomially growing or linearly growing, the strong convergence rate of arbitrarily close to one half is established at a single time
T
or over a time interval 0,
T
, respectively. In both situations, the common one-sided Lipschitz and polynomial growth conditions for the drift coefficients are not required. Two examples are provided to illustrate the theory.
Si-Wu-Tang (SWT), a traditional Chinese medicine formula firstly recorded from the Tang dynasty, has been reported to alleviate gynecological and liver diseases. We preliminarily demonstrated that ...SWT could improve liver fibrosis via modulating intestinal microbiota, but little was known about the mechanisms linking its therapeutic effects to the reshaped immune microenvironment within fibrotic livers. Thus, we established a bile duct ligation (BDL)-induced liver fibrosis murine model to evaluate the hepatoprotective effects and potential mechanisms of SWT. The high-performance liquid chromatography, RNA sequencing and other molecular biological techniques were also performed in our study. Our data demonstrated that SWT significantly improved BDL-induced liver fibrosis and inflammatory responses by inhibiting the expression of genes associated with extracellular matrix synthesis and degradation. Combined with the analysis of immune cell infiltration and gene set enrichment analysis (GSEA), we found that SWT remarkably repaired the unbalanced immune microenvironment by modulating the biological functions of different immune cells, especially for macrophages, neutrophils and CD8+ T cells. In addition, SWT significantly inhibited the activation of M2-like macrophages to reduce the release of profibrotic-cytokines and prevented the activation of neutrophils to suppress neutrophil extracellular trap formation. SWT also efficiently promoted the apoptosis of activated hepatic stellate cells via Fas/FasL signaling pathway, which might be mediated by CD8+ tissue-resident memory T cells. In conclusion, our research not only unraveled the intricate mechanisms underlying the hepatoprotective activities of SWT against liver fibrosis but also provided a novel therapeutic strategy for the treatment of liver fibrosis and its relative complications.
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•Si-Wu-Tang (SWT) exerted an anti-hepatic fibrosis effect on bile duct ligation-induced liver fibrosis•SWT restored the immune microenvironment in livers via regulating multiple immune cells, especially for macrophages, neutrophils and CD8+T cells•SWT might increase the number of CD8+T cells and promote activated HSCs apoptosis through the activation of FAS pathway
The state of charge (SOC) of a battery is a key parameter of electrical vehicles (EVs). However, limited by the lack of computing resources, the SOC estimation strategy used in vehicle-mounted ...battery management systems (V-BMS) is usually simplified. With the development of the new energy vehicle big data platforms, it is possible to obtain the battery SOC through cloud-based BMS (C-BMS). In this paper, a battery SOC estimation method based on common feature extraction and transfer learning is proposed for C-BMS applications. Considering the diversity of driving cycles, a common feature extraction method combining empirical mode decomposition (EMD) and a compensation strategy for C-BMS is designed. The selected features are treated as the new inputs of the SOC estimation model to improve the generalization ability. Subsequently, a long short-term memory (LSTM) recurrent neural network is used to construct a basic model for battery SOC estimation. A parameter-based transfer learning method and an adaptive weighting strategy are used to obtain the C-BMS battery SOC estimation model. Finally, the SOC estimation method is validated on laboratory datasets and cloud platform datasets. The maximum root-mean-square error (RMSE) of battery SOC estimation with the laboratory dataset is 2.2%. The maximum RMSE of battery pack SOC estimation on two different electric vehicles is 1.3%.
Cocaine- and amphetamine-regulated transcript (CART), discovered initially by via differential display RT-PCR analysis of brains of rats administered cocaine, is expressed mainly in central nervous ...system or neuronal origin cells, and is involved in a wide range of behaviors, such as regulation of food intake, energy homeostasis, and reproduction. The hens egg-laying rate mainly depends on the developmental status of follicles, expression of CART have not been identified from hen follicles, the regulatory mechanisms of CART biological activities are still unknown. The objective of this study was to characterize the mRNA expression of CART in hen follicular granulosa cells and determine CART peptide localization and regulatory role during follicular development.
Small white follicles (1-2 mm in diameter) were treated for RNA isolation; Small white follicles (1-2 mm in diameter) and large white follicles (4-6 mm in diameter) were treated for immunohistochemical localization and large white follicles (4-6 mm in diameter), small yellow follicles (6-8 mm in diameter), large yellow follicles (9-12 mm in diameter), mature follicles (F5, F4, F3, F2, F1, >12 mm in diameter) were treated for RNA isolation and Real time PCR.
The results showed that full length of the CDS of hen CART was 336 bp encoding a 111 amino acid polypeptide. In the hen ovary, CART peptide was primarily localized to the theca layer, but not all, the oocyte and granulosa layer, with diffused, weaker staining than relative to the theca cell layer. Further, amount of CART mRNA was more (P < 0.05) in granulosa cells of 6-8 mm follicles compared with that in granulosa cells of other follicles. However, CART mRNA amount was greater in theca cells of 4-6 mm follicles relative to follicles of other sizes (P < 0.05).
Results suggest that CART could play a potential role in developmental regulation of chicken follicles.
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