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.

A Flow Cytometry-Based Assay for the Measurement of Total Complement Activity in the Serum and Clinical Practice

Objective. To develop a novel sensitive and accurate assay suitable for high-volume testing of the total complement activity in the serum for clinical laboratories. Methods. The total complement activity (TCA) to be measured was quantified by detecting the number of fragments produced by erythrocyte lysis and the erythrocyte fragmentation index (EFI), indicating TCA. EFI = M × M 2 / M 1 + M 2 , where M is the number of erythrocyte fragments (removed from the background), M 1 is the number of unagglutinated red cells, M 2 is the number of agglutinated red cell groups, and M 2 / M 1 + M 2 is the agglutination coefficient indicating the degree of erythrocyte agglutination. Mild changes in hemolysin and erythrocyte concentrations were made to optimize the testing conditions. The same serum samples were tested for 10 consecutive days to determine the stability of the experimental results. Serum EFI was detected in both nephrotic syndrome patients and healthy subjects. Results. There was a linear relationship between hemolysin and erythrocyte agglutination ( r = 0.999 , P < 0.001 ). A good linear relationship existed between EFI and TCA ( r = 0.991 , P < 0.001 ). The results were not affected by slight fluctuations in the concentrations of hemolysin or erythrocytes. The interbatch CV = 8.6 % of the test results showed good stability. There was a significant difference in the EFI between nephrotic syndrome patients and healthy individuals, P < 0.001 , and EFI was reduced in nephrotic syndrome patients compared to healthy individuals. Conclusion. The flow cytometry-based assay for TCA was sensitive and accurate and had potential value for clinical application.

Aspek-aspek dalam Menyusun Bahan Ajar Komsel: Suatu Usulan bagi Gereja Penyebaran Injil Majalengka

Cell group is one of the most basic forms of service that must be developed by the church in helping the congregation it serves so that it becomes more mature and grows in the knowledge of God. In addition to praise, prayer, and worship, teaching is an important thing in the conduct of cell groups. In connection with teaching, the church must prepare teaching materials that are relevant to the needs of cell grup members. The goal is to make cell grup members more interested in participating in cell groups, and to become more mature in God and able to face the challenges of life that they are facing.The research method used is library research. This method collects data and information in the form of documents, data archives and other literature information. This paper is expected to help churches in developing cell group's services so that the church has a strong concept in compiling teaching materials that are relevant to cell group's members. This paper will discuss the nature of cell group, the importance of teaching materials relevant to cell group members, the biblical basis of teaching materials relevant to cell group members, and ends with concepts in compiling teaching materials in cell group.

Efficient pre-processing of Single-cell ATAC-seq data

The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 18 times faster than Cell Ranger on human samples, and that uses 33% less RAM when 8 CPU threads are used. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signals and interaction traces for cell groups. We demonstrate the utility of scATAK in an exploration of the chromatin regulatory landscape of a healthy adult human brain and show that it can reveal cell-type-specific features. scATAK is available at https://pachterlab.github.io/scATAK/.

Migratory chondroprogenitors retain superior intrinsic chondrogenic potential for regenerative cartilage repair as compared to human fibronectin derived chondroprogenitors

Cell-based therapy for articular hyaline cartilage regeneration predominantly involves the use of mesenchymal stem cells and chondrocytes. However, the regenerated repair tissue is suboptimal due to the formation of mixed hyaline and fibrocartilage, resulting in inferior long-term functional outcomes. Current preclinical research points towards the potential use of cartilage-derived chondroprogenitors as a viable option for cartilage healing. Fibronectin adhesion assay-derived chondroprogenitors (FAA-CP) and migratory chondroprogenitors (MCP) exhibit features suitable for neocartilage formation but are isolated using distinct protocols. In order to assess superiority between the two cell groups, this study was the first attempt to compare human FAA-CPs with MCPs in normoxic and hypoxic culture conditions, investigating their growth characteristics, surface marker profile and trilineage potency. Their chondrogenic potential was assessed using mRNA expression for markers of chondrogenesis and hypertrophy, glycosaminoglycan content (GAG), and histological staining. MCPs displayed lower levels of hypertrophy markers (RUNX2 and COL1A1), with normoxia-MCP exhibiting significantly higher levels of chondrogenic markers (Aggrecan and COL2A1/COL1A1 ratio), thus showing superior potential towards cartilage repair. Upon chondrogenic induction, normoxia-MCPs also showed significantly higher levels of GAG/DNA with stronger staining. Focused research using MCPs is required as they can be suitable contenders for the generation of hyaline-like repair tissue.

Postnatal morphogenesis of the endocrine part of pancreas of nutria (Myocastor coypus)

The morphological characteristics of the endocrine pancreas of nutria in female and male nutria at the age of 1 day, 2 months, 4.5 months, 7.5 months and 12 months have a number of sex and age characteristics and differences. The number of islets in pancreatic lobules at 1 day of age in males is 3–6, and in females – 2–3. At 2 months of age, their number in males decreases to 2–4, and in females it increases to 5–6. From 4.5 months of age, the number of islands in females and males ranges from 2 to 3. At 1 day and 2 months of age, in nutria, islet division is recorded due to the cleavage and formation of new cell groups. The area of endocrine islets in females and males of nutria changes in a wave-like manner with age, having minimum values in 1-day and 4.5 months of age, and the maximum at 12 months of age. The nuclear-cytoplasmic ratio of endocrine islet insulocytes in nutria increases from 1 day to 2 months of life to their maximum values, and in subsequent age periods decrease and by the age of 12 months reaches its minimum.

Specifically Binding of D-Amino Neuraminic Acid to Ganglioside Studied in Prostate Cancer Cells

Aims: The aim of this study was to investigate the cellular binding site of human KDN (2-keto-3-deoxy-D-glycero-D-galacto-nononic acid). The KDN molecule is a member of the sialic acid family, and its expression increases in cancer cells. Although KDN has been shown to bind to GM3 (Monosialodihexosyl Ganglioside) in trout sperm.Methods and Results: Prostate cancer cell line (DU145) was used in this study. Each experimental group was divided into 3 groups: control, GCS (Glucosylceramide synthase) enzyme inhibitor Genz-123346 treated, and GM3 synthesis inhibitor Triptolide treated. Each group was stained using the immunocytochemical method for GM3, GD3 (Disialosyllactosylceramide) and KDN. The FTIR (Fourier Transform Infrared Spectroscopy) analysis was performed to elucidate the cellular changes upon treatment. The non-treated number 1 cell group stained positive with all of GM3, GD3 and KDN, the GCS enzyme blocked with Genz-123346 number 2 cell groups stained positive with only KDN. Furthermore, GD3 Synthase inhibitor Triptolide treated number 3 cell group stained positive with GM3 and KDN. Measurements with FTIR showed apoptotic features with Triptolide while Genz-123346 had no negative effect on the cell viability. The decrease in sugar constructions was revealed and the results that we obtained with staining were reinforced.Conclusions: Determining the location of bounded KDN is important in selecting new targets for cancer treatment researches. It has been shown that KDN is not inhibited by both GM3 inhibition and GD3 inhibition, and thus, KDN might also bind to different places, be specific, and not only attached to any of gangliosides of the GM or GD series.

Nested Stochastic Block Models applied to the analysis of single cell data

Single cell profiling has been proven to be a powerful tool in molecular biology to understand the complex behaviours of heterogeneous system. The definition of the properties of single cells is the primary endpoint of such analysis, cells are typically clustered to underpin the common determinants that can be used to describe functional properties of the cell mixture under investigation. Several approaches have been proposed to identify cell clusters; while this is matter of active research, one popular approach is based on community detection in neighbourhood graphs by optimisation of modularity. In this paper we propose an alternative and principled solution to this problem, based on Stochastic Block Models. We show that such approach not only is suitable for identification of cell groups, it also provides a solid framework to perform other relevant tasks in single cell analysis, such as label transfer. To encourage the use of Stochastic Block Models, we developed a python library, , that is compatible with the popular framework.

Role and Mechanism of Exosome CircRNA Eukaryotic Translation Initiation Factor 4γ2 (EIF4G2) in Cervical Cancer

Our work was to evaluate Exosome CircRNA EIF4G2 in cervical cancer development. Methods: Using Hela and Siha in our present study. Transfection vector, exosome cirEIF4G2, exosome si-NC or exosome si-circEIF4G2 in cells. Using RT-qPCR to measure circEIF4G2 gene expression in difference cell groups. Evaluating cell proliferation, apoptosis, invasion and wound healing rate by MTT, flow cytometry, transwell and wound healing assay. The relative proteins, HPV16 E6 and HPV16 E7, were evaluated by WB assay. With Exosome CircRNA EIF4G2 transfection, Hela and Siha cells proliferation, invasion cells number and wound healing rates were significantly increased and cells apoptosis were significantly depressed (P < 0.001, respectively) with HPV16 E6 and HPV16 E7 proteins expression were significantly up-regulation (P < 0.001, respectively). However, with Exosome si-CircRNA EIF4G2 transfection, Hela and Siha cells proliferation, invasion cells number and wound healing rates were significantly depressed and cells apoptosis were significantly increased (P < 0.001, respectively) with HPV16 E6 and HPV16 E7 proteins expression were significantly down-regulation (P < 0.001, respectively). Exosome CircRNA EIF4G2 as an oncology role in cervical cancer via regulation HPV16 E6/E7 up-regulation in vitro study.

Deep Categorization of Blood Cells u sing Depthwise Convolutions

Modern-day computation has become indispensable in the healthcare industry. From medical image processing to cost reduction, Artificial Intelligence has proved its significance in solving complex healthcare problems. One of the primary areas in which it can be of greater use in hematology. Categorization of white-blood cells is imperative to pre-identify abnormalities. Through this paper, we collected image samples for 4 major White Blood cell groups, which are Neutrophils, Lymphocytes, Monocytes, and Eosinophils. The aim of this research is to put forward an intelligent system that efficiently alleviates the stringent requirement of a cytological study. The proposed system classifies 4 white-blood-cell types based on their morphological variation. With the experimental modulations that we chose to integrate, the presented model attained an accuracy of 97%.