Interfacial immobilization of monoclonal antibody and detection of human prostate-specific antigen.

Antibody orientation and its antigen binding efficiency at interface are of particular interest in many immunoassays and biosensor applications. In this paper, spectroscopic ellipsometry (SE), neutron reflection (NR), and dual polarization interferometry (DPI) have been used to investigate interfacial assembly of the antibody [mouse monoclonal anti-human prostate-specific antigen (anti-hPSA)] at the silicon oxide/water interface and subsequent antigen binding. It was found that the mass density of antibody adsorbed at the interface increased with solution concentration and adsorption time while the antigen binding efficiency showed a steady decline with increasing antibody amount at the interface over the concentration range studied. The amount of antigen bound to the interfacial immobilized antibody reached a maximum when the surface-adsorbed amount of antibody was around 1.5 mg/m(2). This phenomenon is well interpreted by the interfacial structural packing or crowding. NR revealed that the Y-shaped antibody laid flat on the interface at low surface mass density with a thickness around 40 Å, equivalent to the short axial length of the antibody molecule. The loose packing of the antibody within this range resulted in better antigen binding efficiency, while the subsequent increase of surface-adsorbed amount led to the crowding or overlapping of antibody fragments, hence reducing the antigen binding due to the steric hindrance. In situ studies of antigen binding by both NR and DPI demonstrated that the antigen inserted into the antibody layer rather than forming an additional layer on the top. Stability assaying revealed that the antibody immobilized at the silica surface remained stable and active over the monitoring period of 4 months. These results are useful in forming a general understanding of antibody interfacial behavior and particularly relevant to the control of their activity and stability in biosensor development.

On-line learning of mutually orthogonal subspaces for face recognition by image sets.

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.