We previously reported a simple method to analyze the interaction of cell-surface molecules in living cells. This method termed enzyme-mediated activation of radical sources (EMARS) is featured by radical formation of the labeling reagent by horseradish peroxidase (HRP). Herein, we propose an approach to the cell-surface molecular interactome by using combination of this EMARS reaction and MS-based proteomics techniques. In the current study, we employed a novel labeling reagent, fluorescein-conjugated arylazide. The fluorescein-tagged proteins resulting from the EMARS reaction were directly detected in the electrophoresis gels with a fluorescence image analyzer. These products were also purified and concentrated by immunoaffinity chromatography with anti-fluorescein antibody-immobilized resins. The purified fluorescein-tagged proteins were subsequently subjected to an MS-based proteomics analysis. Analysis using HRP-conjugated cholera toxin subunit B, which recognizes a lipid raft marker, ganglioside GM1, revealed 30 membrane and secreted proteins that were candidates for the cell-surface molecules coclustering with GM1. The proposed approach will provide a clue to study functional molecular interactions in a variety of biological events on the cell surface.
Based on the Finite Element Method and wheel-rail force measurement theory, the effects of different drilling schemes and parameters on the output of the measuring electrical bridge are analyzed, and the optimal parameters are determined. The finite element model of a metro instrumented wheel-set is established by software ABAQUS. The effects of drilling radius, hole size and wheel-rail contact point on the strain of measuring points are calculated and analyzed, and the measuring bridge outputs of two different drilling schemes are compared. The output of the wheel-rail forces is predicted based on LSTM long short memory network. The results show that: (1) the optimal drilling position is on the middle surface of the wheel, (2) The drilling scheme of eight-hole is better than that of four-hole, (3) LSTM neural network can realize decoupling of bridge signal and high precision prediction of wheel-rail forces.
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