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Abstract Background Gene interaction networks are graphs in which nodes represent genes and edges represent functional interactions between them. These interactions can be at multiple levels, for instance, gene regulation, protein-protein interaction, or metabolic pathways. To analyse gene interaction networks at a large scale, gene co-expression network analysis is often applied on high-throughput gene expression data such as RNA sequencing data. With the advance in sequencing technology, expression of genes can be measured in individual cells. Single-cell RNA sequencing (scRNAseq) provides insights of cellular development, differentiation and characteristics at the transcriptomic level. High sparsity and high-dimensional data structures pose challenges in scRNAseq data analysis. Results In this study, a sparse inverse covariance matrix estimation framework for scRNAseq data is developed to capture direct functional interactions between genes. Comparative analyses highlight high performance and fast computation of Stein-type shrinkage in high-dimensional data using simulated scRNAseq data. Data transformation approaches also show improvement in performance of shrinkage methods in non-Gaussian distributed data. Zero-inflated modelling of scRNAseq data based on a negative binomial distribution enhances shrinkage performance in zero-inflated data without interference on non zero-inflated count data. Conclusion The proposed framework broadens application of graphical model in scRNAseq analysis with flexibility in sparsity of count data resulting from dropout events, high performance, and fast computational time. Implementation of the framework is in a reproducible Snakemake workflow https://github.com/calathea24/ZINBGraphicalModel and R package ZINBStein https://github.com/calathea24/ZINBStein .

The therapeutic activation of antitumour immunity by immune checkpoint inhibitors (ICIs) is a significant advance in cancer medicine, not least due to the prospect of long-term remission. However, many patients are unresponsive to ICI therapy and may experience serious side effects; companion biomarkers are urgently needed to help inform ICI prescribing decisions. We present the IMMUNETS networks of gene coregulation in five key immune cell types and their application to interrogate control of nivolumab response in advanced melanoma cohorts. The results evidence a role for each of the IMMUNETS cell types in ICI response and in driving tumour clearance with independent cohorts from TCGA. As expected, 'immune hot' status, including T cell proliferation, correlates with response to first-line ICI therapy. Genes regulated in NK, dendritic, and B cells are the most prominent discriminators of nivolumab response in patients that had previously progressed on another ICI. Multivariate analysis controlling for tumour stage and age highlights CIITA and IKZF3 as candidate prognostic biomarkers. IMMUNETS provide a resource for network biology, enabling context-specific analysis of immune components in orthogonal datasets. Overall, our results illuminate the relationship between the tumour microenvironment and clinical trajectories, with potential implications for precision medicine. [ABSTRACT FROM AUTHOR]

Magnetic ZnAl hydrotalcite (mHT) was synthesized by co-precipitation of Zn2+/Al3+ salt mixture in the presence of Fe3O4. mHT nanomaterials were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray diffraction, vibrating-sample magnetometer, and nitrogen adsorption isotherm. Then, mHT was used as support for immobilizing cellulase under an adsorption mechanism to form cell@mHT. The effect of pH, cellulase concentration, the amount of mHT, and immobilization time on the immobilization of cellulase onto mHT were carefully investigated. The enzyme activity of both free cellulase and cell@mHT as well as immobilization efficiency, was analyzed by determination of reduced glucose using DNS as a color indicator. The immobilization process obtained the highest loading efficiency with 99.0% when carried out at pH 5, with a cellulase concentration of 0.1mg/ml and using 0.1 g of mHT. Cell@mHT shows good enzyme activity when reacting with 1 mass% CMC solution at 50°C after 45 minutes with relative enzyme activity of 80.8%. [ABSTRACT FROM AUTHOR]

Mammalian cells have developed several mechanisms to sense viruses and initiate adequate responses such as production of interferons. Interferons activate the antiviral response through the Jak- STAT signalling pathway. To establish a chronic infection, viruses need to counteract this barrier of defence. The hepatitis C and hepatitis B viruses are known to up-regulate the expression of protein phosphatase 2 A ( PP2A). In this study, we show that PP2Ac associates with Jak1/Tyk2/ STAT1 and reduces Jak1/Tyk2/ STAT1 phosphorylation resulting in an impairment of the IFNα-induced HCV antiviral response. Using the fully infectious HCV cell culture system ( HCVcc), we demonstrate that the PP2A catalytic activity is not required to block the antiviral effect of IFNα, although it is needed to support HCVcc replication. Our data suggest an important contribution of virus-induced PP2Ac up-regulation in the establishment of a chronic infection. [ABSTRACT FROM AUTHOR]

This study utilizes large eddy simulation, aircraft measurements, and satellite observations to identify factors that bias the absolute magnitude of metrics of aerosol-cloud-precipitation interactions for warm clouds. The metrics considered are precipitation susceptibility So, which examines rain rate sensitivity to changes in drop number, and a cloud-precipitation metric, X, which relates changes in rain rate to those in drop size. While wide ranges in rain rate exist at fixed cloud drop concentration for different cloud liquid water amounts, X and So are shown to be relatively insensitive to the growth phase of the cloud for large datasets that include data representing the full spectrum of cloud lifetime. Spatial resolution of measurements is shown to influence the liquid water path-dependent behavior of So and X. Other factors of importance are the choice of the minimum rain rate threshold, and how to quantify rain rate, drop size, and the cloud condensation nucleus proxy. Finally, low biases in retrieved aerosol amounts owing to wet scavenging and high biases associated with above-cloud aerosol layers should be accounted for. The paper explores the impact of these effects for model, satellite, and aircraft data. [ABSTRACT FROM AUTHOR]

This study utilizes large eddy simulation, aircraft measurements, and satellite observations to identify factors that bias the absolute magnitude of metrics of aerosol-cloud-precipitation interactions for warm clouds. The metrics considered are precipitation susceptibility So, which examines rain rate sensitivity to changes in drop number, and a cloud-precipitation metric, X, which relates changes in rain rate to those in drop size. While wide ranges in rain rate exist at fixed cloud drop concentration for different cloud liquid water amounts, X and So are shown to be relatively insensitive to the growth phase of the cloud for large datasets that include data representing the full spectrum of cloud lifetime. Spatial resolution of measurements is shown to influence both the magnitude and liquid water path-dependent behavior of So and X. Other factors of importance are the choice of how to quantify rain rate, drop size, and the cloud condensation nucleus proxy. Finally, low biases in retrieved aerosol amounts owing to wet scavenging and high biases associated with above-cloud aerosol layers should be accounted for. The paper explores the impact of these effects for model, satellite, and aircraft data. [ABSTRACT FROM AUTHOR]