A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data

Here, we present a multi-modal deep generative model, the single-cell Multi-View Profiler (scMVP), which is designed for handling sequencing data that simultaneously measure gene expression and chromatin accessibility in the same cell, including SNARE-seq, sci-CAR, Paired-seq, SHARE-seq, and Multiome from 10X Genomics. scMVP generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification. scMVP can help mitigate data sparsity issues with imputation and accurately identify cell groups for different joint profiling techniques with common latent embedding, and we demonstrate its advantages on several realistic datasets.

RPP25 as a Prognostic-Related Biomarker That Correlates With Tumor Metabolism in Glioblastoma

RPP25, a 25 kDa protein subunit of ribonuclease P (RNase P), is a protein-coding gene. Disorders associated with RPP25 include chromosome 15Q24 deletion syndrome and diffuse scleroderma, while systemic sclerosis can be complicated by malignancy. However, the functional role of RPP25 expression in glioblastoma multiforme (GBM) is unclear. In this study, comprehensive bioinformatics analysis was used to evaluate the impact of RPP25 on GBM occurrence and prognosis. Differential analysis of multiple databases showed that RPP25 was commonly highly expressed in multiple cancers but lowly expressed in GBM. Survival prognostic results showed that RPP25 was prognostically relevant in six tumors (CESC, GBM, LAML, LUAD, SKCM, and UVM), but high RPP25 expression was significantly associated with poor patient prognosis except for CESC. Analysis of RPP25 expression in GBM alone revealed that RPP25 was significantly downregulated in GBM compared with normal tissue. Receiver operating characteristic (ROC) combined with Kaplan-Meier (KM) analysis and Cox regression analysis showed that high RPP25 expression was a prognostic risk factor for GBM and had a predictive value for the 1-year, 2-year, and 3-year survival of GBM patients. In addition, the expression of RPP25 was correlated with the level of immune cell infiltration. The gene set enrichment analysis (GSEA) results showed that RPP25 was mainly associated with signalling pathways related to tumor progression and tumor metabolism.

Differential Analysis of Fault Currents in a Power Distribution Feeder Using abc, αβ0, and dq0 Reference Frames

This paper proposes a methodology to monitor the instantaneous value of the current and its derivative in the abc, αβ0, and dq0 reference frames to act in the detection of fault current in medium-voltage distribution systems. The method employed to calculate the derivative was Euler’s, with processing sampling rates of 10, 50, 100, and 200 μs. Using the MATLAB/Simulink platform, fault situations were analyzed on a real feeder of approximately 1.1132 km in length, fed by an 11.4 kV source, composed of 26 unbalanced loads and modeled as constant power. The simulation results show that the detection occurred in the different fault situations implemented in the feeder and that the detection speed is related to the value of the processing sampling rate (PSR) used. Considering all fault situations and regardless of the PSR value used, the total average detection time was 49 µs. Besides that, the joint action of the detection system with the Thyristor Controlled Series Capacitor (TCSC) limited the fault current in each situation. The average detection time for each fault situation analyzed was below the typical time for a recloser to act, regardless of the reference adopted for the analysis.

Monocyte distribution width (MDW): a useful biomarker to improve sepsis management in Emergency Department

Objectives Sepsis is a time-dependent and life-threating condition. Despite several biomarkers are available, none of them is completely reliable for the diagnosis. This study aimed to evaluate the diagnostic utility of monocyte distribution width (MDW) to early detect sepsis in adult patients admitted in the Emergency Department (ED) with a five part differential analysis as part of the standard clinical practice. Methods A prospective cohort study was conducted on 985 patients aged from 18 to 96 and included in the study between November 2019 and December 2019. Enrolled subjects were classified into four groups based on sepsis-2 diagnostic criteria: control, Systemic Inflammatory Response Syndrome (SIRS), infection and sepsis. The hematology analyzer DxH 900 (Beckman Coulter Inc.) provides the new reportable parameter MDW, included in the leukocyte 5 part differential analysis, cleared by Food and Drug administration (FDA) and European Community In-Vitro-Diagnostic Medical Device (CE IVD) marked as early sepsis indicator (ESId). Results MDW was able to differentiate the sepsis group from all other groups with Area Under the Curve (AUC) of 0.849, sensitivity of 87.3% and specificity of 71.7% at cut-off of 20.1. MDW in combination with white blood cell (WBC) improves the performance for sepsis detection with a sensitivity increased up to 96.8% when at least one of the two biomarkers are abnormal, and a specificity increased up to 94.6% when both biomarkers are abnormal. Conclusions MDW can predict sepsis increasing the clinical value of Leukocyte 5 Part Differential analysis and supporting the clinical decision making in sepsis management at the admission to the ED.

Prediction of Hepatocellular Carcinoma Prognosis and Immune Cell Infiltration Using Gene Signature Associated with Inflammatory Response

It has been demonstrated that the inflammatory response influences cancer development and can be used as a prognostic biomarker in various tumors. However, the relevance of genes associated with inflammatory responses in hepatocellular carcinoma (HCC) remains unknown. The Cancer Genome Atlas (TCGA) database was analyzed using weighted gene coexpression network analysis (WGCNA) and differential analysis to discover essential inflammatory response-related genes (IFRGs). Cox regression studies, both univariate and multivariate, were employed to develop a prognostic IFRGs signature. Additionally, Gene Set Enrichment Analysis (GSEA) was used to deduce the biological function of the IFRGs signature. Finally, we estimated immune cell infiltration using a single sample GSEA (ssGSEA) and x-cell. Our results revealed that, among the major HCC IFRGs, two (DNASE1L3 and KLKB1) were employed to create a predictive IFRG signature. The IFRG signature could correctly predict overall survival (O.S) as per Kaplan-Meier time-dependent roc curves analysis. It was also linked to pathological tumor stage and T stage and might be used as a prognostic predictor in HCC. GSEA analysis concluded that the IFRG signature might influence the immune response in HCC. Immunological cell infiltration and immune checkpoint molecule expression differed in the high-risk and low-risk groups. As a result of our findings, DNASILE may play a role in the tumor microenvironment. However, more research is necessary to confirm the role of DNASE1L3 and KLKB1.

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma

Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM.

Dysbiosis of vaginal microbiota associated with persistent high-risk human papilloma virus infection

Background The status of vaginal microbiota in persistent high-risk human papilloma virus (HR-HPV) infection is unclear. The present work aimed to identify the vaginal microbiota of persistent HPV infection and explore the possible underlying microbiota factors. Methods A total of 100 women were recruited in this study, of which 28 presented HR-HPV persistent infection (P group), 30 showed clearance of any subtype of HR-HPV (C group), and 42 had no history of any HR-HPV infection (NC group). The vaginal microbiota and the community structure of the three groups were compared based on the 16S rRNA sequencing of the V3–V4 region. The microbiota diversity and differential analysis were carried out to detect the potential factors associated with HR-HPV infection. Results P and C groups showed an increase of Firmicutes and Actinobacteriota but a decrease in Proteobacteria compared to the NC group. The Chao1 index indicated that the microbial richness of the NC group was greater than C group (P < 0.05).The principal co-ordinate analysis(PCoA) revealed differences between the NC and P/C groups.The linear discriminant analysis effect size (LEfSe) method indicated that Proteobacteria phylum was significantly different in the mean relative abundance in the NC group,but the P and C groups did not show such indicative taxa. The Wilcox rank-sum test indicated that the Bifidobacterium (P = 0.002) and Lactobacillus (P = 0.005) of the C group were in a high mean relative abundance compared to the NC group. Conclusions The persistent HR-HPV infection is associated with dysbiosis of the vaginal microbiota. Microbiome regulation with Bifidobacteria and Lactobacillus may affect the clearance of HPV.

Identification of Pueraria spp. through DNA barcoding and comparative transcriptomics

Background Kudzu is a term used generically to describe members of the genus Pueraria. Kudzu roots have been used for centuries in traditional Chinese medicine in view of their high levels of beneficial isoflavones including the unique 8-C-glycoside of daidzein, puerarin. In the US, kudzu is seen as a noxious weed causing ecological and economic damage. However, not all kudzu species make puerarin or are equally invasive. Kudzu remains difficult to identify due to its diverse morphology and inconsistent nomenclature. Results We have generated sequences for the internal transcribed spacer 2 (ITS2) and maturase K (matK) regions of Pueraria montana lobata, P. montana montana, and P. phaseoloides, and identified two accessions previously used for differential analysis of puerarin biosynthesis as P. lobata and P. phaseoloides. Additionally, we have generated root transcriptomes for the puerarin-producing P. m. lobata and the non-puerarin producing P. phaseoloides. Within the transcriptomes, microsatellites were identified to aid in species identification as well as population diversity. Conclusions The barcode sequences generated will aid in fast and efficient identification of the three kudzu species. Additionally, the microsatellites identified from the transcriptomes will aid in genetic analysis. The root transcriptomes also provide a molecular toolkit for comparative gene expression analysis towards elucidation of the biosynthesis of kudzu phytochemicals.

Identification and Verification of Five Potential Biomarkers Related to Skin and Thermal Injury Using Weighted Gene Co-Expression Network Analysis

Background: Burn injury is a life-threatening disease that does not have ideal biomarkers. Therefore, this study first applied weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening methods to identify pivotal genes and diagnostic biomarkers associated with the skin burn process.Methods: After obtaining transcriptomic datasets of burn patient skin and normal skin from Gene Expression Omnibus (GEO) and performing differential analysis and functional enrichment, WGCNA was used to identify hub gene modules associated with burn skin processes in the burn patient peripheral blood sample dataset and determine the correlation between modules and clinical features. Enrichment analysis was performed to identify the functions and pathways of key module genes. Differential analysis, WGCNA, protein-protein interaction analysis, and enrichment analysis were utilized to screen for hub genes. Hub genes were validated in two other GEO datasets, tested by immunohistochemistry for hub gene expression in burn patients, and receiver operating characteristic curve analysis was performed. Finally, we constructed the specific drug activity, transcription factors, and microRNA regulatory network of the five hub genes.Results: A total of 1,373 DEGs in GSE8056 were obtained, and the top 5 upregulated genes were S100A12, CXCL8, CXCL5, MMP3, and MMP1, whereas the top 5 downregulated genes were SCGB1D2, SCGB2A2, DCD, TSPAN8, and KRT25. DEGs were significantly enriched in the immunity, epidermal development, and skin development processes. In WGCNA, the yellow module was identified as the most closely associated module with tissue damage during the burn process, and the five hub genes (ANXA3, MCEMP1, MMP9, S100A12, and TCN1) were identified as the key genes for burn injury status, which consistently showed high expression in burn patient blood samples in the GSE37069 and GSE13902 datasets. Furthermore, we verified using immunohistochemistry that these five novel hub genes were also significantly elevated in burn patient skin. In addition, MCEMP1, MMP9, and S100A12 showed perfect diagnostic performance in the receiver operating characteristic analysis.Conclusion: In conclusion, we analyzed the changes in genetic processes in the skin during burns and used them to identify five potential novel diagnostic markers in blood samples from burn patients, which are important for burn patient diagnosis. In particular, MCEMP1, MMP9, and S100A12 are three key blood biomarkers that can be used to identify skin damage in burn patients.

Differential Analysis of Volumetric Strain Method Characterization in the Context of Phase Change of Water in Carbonate Rocks

Modernized technological processes or increasing demands on building materials force the scientific community to analyze in more detail the suitability of individual raw materials and deposits. New or modernized research methodologies make it possible to better understand not only the geometrical structure of the pore space of materials but also the processes taking place in them and the interaction of many factors at the same time. Despite the extensive literature in the field of research on capillary-porous materials, scientists still face many challenges because not everything is known. Carbonate rocks are the most common (one-tenth of Earth’s crust) sedimentary rocks. Analysis of the test results obtained with the use of the modernized differentia analysis of volumetric strain (DAVS) methodology allows for a better adjustment of rock deposits to the products that can be produced from them. In this manner, it is possible that it will contribute to a more rational use of exhaustible rock deposits and not only carbonate ones. This research subject is of great importance for modern science, which was also noted in many of science publications.