Search

In conventional subspace clustering methods, affinity matrix learning and spectral clustering algorithms are widely used for clustering tasks. However, these steps face issues, including high time consumption and spatial complexity, making large-scale subspace clustering (LS 2 C) tasks challenging to execute effectively. To address these issues, we propose a large-scale subspace clustering method based on pure kernel tensor learning (PKTLS 2 C). Specifically, we design a pure kernel tensor learning (PKT) method to acquire as much data feature information as possible while ensuring model robustness. Next, we extract a small sample dataset from the original data and use PKT to learn its affinity matrix while simultaneously training a deep encoder. Finally, we apply the trained deep encoder to the original large-scale dataset to quickly obtain its projection sparse coding representation and perform clustering. Through extensive experiments on large-scale real datasets, we demonstrate that the PKTLS 2 C method outperforms existing LS 2 C methods in clustering performance. [ABSTRACT FROM AUTHOR]

The interaction between coniferyl alcohol (CA) and laccase (LAC) was investigated using molecular dynamics (MD) simulations and spectral experiments. The mode of interaction between CA and LAC was established by MD simulations. The micro-environmental changes, stability and rigidity of the LAC-CA system were assessed by relevant parameters. These parameters include root mean square deviation (RMSD), root mean square fluctuation (RMSF) and radius of gyration (Rg). The calculated binding free energy (ΔGbinding= −19.99 kcal·mol.−1), the van der waals (VDW) contribution (ΔGvdw=−23.99 kcal·mol−1) and the electrostatic energy (ΔGele=−23.09 kcal·mol−1) of LAC-CA system demonstrated that the interaction of LAC-CA was a spontaneous process and the main interaction forces were van der Waal's and electrostatic forces. The values of ΔGvdw and ΔGele were negative, which demonstrated that VDW interactions and electrostatic interactions were favorable for the binding of CA and LAC. The binding constants, thermodynamic parameters, molecular force types and binding distances confirmed the interaction between CA and LAC and further verified the rationality of the theoretical model by spectral experiments. The MD simulations and experimental approaches provide clues for the discovery of new mediators and useful references for the mechanism of microbial degradation of lignin and industrialization of lignocellulose. [ABSTRACT FROM AUTHOR]