Glycan Sequencing Based on Glycosidase-Assisted Nanopore Sensing.

Nanopores are promising sensors for glycan analysis with the accurate identification of complex glycans laying the foundation for nanopore-based sequencing. However, their applicability toward continuous glycan sequencing has not yet been demonstrated. Here, we present a proof-of-concept of glycan sequencing by combining nanopore technology with glycosidase-hydrolyzing reactions.

DecentralDC: Assessing data contribution under decentralized sharing and exchange blockchain.

The issue of data quality has emerged as a critical concern, as low-quality data can impede data sharing, diminish intrinsic value, and result in economic losses. Current research on data quality assessment primarily focuses on four dimensions: intrinsic, contextual, presentational, and accessibility quality, with intrinsic and presentational quality mainly centered on data content, and contextual quality reflecting data usage scenarios. However, existing approaches lack consideration for the behavior of data within specific application scenarios, which encompasses the degree of participation and support of data within a given scenario, offering valuable insights for optimizing resource deployment and business processes.

Nanopore-based glycan sequencing: state of the art and future prospects.

Sequencing of biomacromolecules is a crucial cornerstone in life sciences. Glycans, one of the fundamental biomolecules, derive their physiological and pathological functions from their structures. Glycan sequencing faces challenges due to its structural complexity and current detection technology limitations.

Direct Identification of Complex Glycans via a Highly Sensitive Engineered Nanopore.

The crucial roles that glycans play in biological systems are determined by their structures. However, the analysis of glycan structures still has numerous bottlenecks due to their inherent complexities. The nanopore technology has emerged as a powerful sensor for DNA sequencing and peptide detection.

Matching biomedical ontologies with GCN-based feature propagation.

With an increasing number of biomedical ontologies being evolved independently, matching these ontologies to solve the interoperability problem has become a critical issue in biomedical applications. Traditional biomedical ontology matching methods are mostly based on rules or similarities for concepts and properties. These approaches require manually designed rules that not only fail to address the heterogeneity of domain ontology terminology and the ambiguity of multiple meanings of words, but also make it difficult to capture structural information in ontologies that contain a large amount of semantics during matching.