Boosting scRNA-seq data clustering by cluster-aware feature weighting.

Background: The rapid development of single-cell RNA sequencing (scRNA-seq) enables the exploration of cell heterogeneity, which is usually done by scRNA-seq data clustering. The essence of scRNA-seq data clustering is to group cells by measuring the similarities among genes/transcripts of cells. And the selection of features for cell similarity evaluation is of great importance, which will significantly impact clustering effectiveness and efficiency.

Enzymatic Extraction of Bioactive and Self-Assembling Wool Keratin for Biomedical Applications.

Keratin is widely recognized as a high-quality renewable protein resource for biomedical applications. Despite their extensive existence, keratin resources such as feathers, wool, and hair exhibit high stability and mechanical properties because of their high disulfide bond content. Consequently, keratin extraction is challenging and its application is greatly hindered.

Fabrication and characterization of high molecular keratin based nanofibrous membranes for wound healing.

Keratin is widely used in the biomaterial application, but the keratin prepared by the physical or chemical approach has relatively low molecular weight and mechanical properties. Here we report the preparation of high molecular keratin (HMK) with molecular weight of 120 kDa via multi-enzyme cascade pathway and its application in wound healing. Briefly, we prepared the soluble keratin from wool by keratinase and improved the molecular weight of keratin by transglutaminase (TGase).

PhosD: inferring kinase-substrate interactions based on protein domains.

Motivation: Identifying the kinase-substrate relationships is vital to understanding the phosphorylation events and various biological processes, especially signal transductions. Although large amount of phosphorylation sites have been detected, unfortunately, it is rarely known which kinases activate those sites. Despite distinct computational approaches have been proposed to predict the kinase-substrate interactions, the prediction accuracy still needs to be improved.

Identifying Disease Associated miRNAs Based on Protein Domains.

MicroRNAs (miRNAs) are a class of small endogenous non-coding genes, acting as regulators in the post-transcriptional processes. Recently, the miRNAs are found to be widely involved in different types of diseases. Therefore, the identification of disease associated miRNAs can help understand the mechanisms that underlie the disease and identify new biomarkers.

[Factors affecting the DAPI fluorescence direct count in the tidal river sediment].

The factors affecting the DAPI (4', 6-diamidino-2-phenylidole) fluorescence direct count in the tidal river sediment were examined. Sediment samples were collected from the Guangzhou section of the Pearl River. Besides sediment texture and organic matter, an improved staining procedure and the involved parameters were analyzed.