Antibody solutions deviate from the dynamical and rheological response expected for globular proteins, especially as volume fraction is increased. Experimental evidence shows that antibodies can reversibly bind to each other via F ab and F c domains, and form larger structures (clusters) of several antibodies. Here we present a microscopic equilibrium model to account for the distribution of cluster sizes. Antibody clusters are modeled as polymers that can grow via reversible bonds either between two F ab domains or between a F ab and a F c. We propose that the dynamical and rheological behavior is determined by molecular entanglements of the clusters. This entanglement does not occur at low concentrations where antibody-antibody binding contributes to the viscosity by increasing the effective size of the particles. The model explains the observed shear-thinning behavior of antibody solutions.

No abstract

Skills management is one of the key factors to address the increasing competitiveness among different companies. Suitable knowledge representation and approach for matching skills and competences in job vacancies and candidate profiles can support human resources management automation through suitable matching and ranking services. This paper presents an approach for matchmaking between skills demand and supply through skill profiles enrichment and matching supply and demand profiles over multiple criteria. This work builds upon methods for profile modeling, information enrichment and multi-criteria matching. The main contribution of this work is a methodology for harmonization and enrichment of heterogeneous profile models and skill set description by making use of the standard ESCO ontology. Secondly, an algorithm is proposed for similarity matching across multi-criteria for discovering set of profiles that best fits the job description criteria. A prototype web-based system has been developed to implement the proposed approach and deployed online. The system has been tested with real IT jobs related dataset and validated against relevance scores provided by human experts. Experimental results show consistent correspondence between the similarity ranking scores produced by the system and scores provided by the human users.