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The purpose of this work was to elucidate the molecular interactions leading to monoclonal antibody self-association and precipitation and utilize biophysical measurements to predict solubility behavior at high protein concentration. Two monoclonal antibodies (mAb-G and mAb-R) binding to overlapping epitopes were investigated. Precipitation of mAb-G solutions was most prominent at high ionic strength conditions and demonstrated strong dependence on ionic strength, as well as slight dependence on solution pH. At similar conditions no precipitation was observed for mAb-R solutions. Intermolecular interactions (interaction parameter, kD) related well with high concentration solubility behavior of both antibodies. Upon increasing buffer ionic strength, interactions of mAb-R tended to weaken, while those of mAb-G became more attractive. To investigate the role of amino acid sequence on precipitation behavior, mutants were designed by substituting the CDR of mAb-R into the mAb-G framework (GM-1) or deleting two hydrophobic residues in the CDR of mAb-G (GM-2). No precipitation was observed at high ionic strength for either mutant. The molecular interactions of mutants were similar in magnitude to those of mAb-R. The results suggest that presence of hydrophobic groups in the CDR of mAb-G may be responsible for compromising its solubility at high ionic strength conditions since deleting these residues mitigated the solubility issue.

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Abstract. Biometric authentication using mobile devices is becoming a convenient and important means to secure access to remote services such as telebanking and electronic transactions. Such an application poses a very challenging pattern recognition problem: the training samples are often sparse and they cannot represent the biometrics of a person. The query features are easily affected by the acquisition environment, the user's accessories, occlusions and aging. Semi-supervised learning -learning from the query/test data -can be a means to tap the vast unlabeled training data. While there is evidence that semi-supervised learning can work in text categorization and biometrics, its application on mobile devices remains a great challenge. As a preliminary, yet, indispensable study towards the goal of semi-supervised learning, we analyze the following sub-problems: model adaptation, update criteria, inference with several models and user-specific time-dependent performance assessment, and explore possible solutions and research directions.