Role of secretory immunoglobulin A and secretory component in the protection of mucosal surfaces.

The contribution of secretory immunoglobulin A (SIgA) antibodies in the defense of mucosal epithelia plays an important role in preventing pathogen adhesion to host cells, therefore blocking dissemination and further infection. This mechanism, referred to as immune exclusion, represents the dominant mode of action of the antibody. However, SIgA antibodies combine multiple facets, which together confer properties extending from intracellular and serosal neutralization of antigens, activation of non-inflammatory pathways and homeostatic control of the endogenous microbiota. The sum of these features suggests that future opportunities for translational application from research-based knowledge to clinics include the mucosal delivery of bioactive antibodies capable of preserving immunoreactivity in the lung, gastrointestinal tract, the genito-urinary tract for the treatment of infections. This article covers topics dealing with the structure of SIgA, the dissection of its mode of action in epithelia lining different mucosal surfaces and its potential in immunotherapy against infectious pathogens.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.