The neonatal Fc receptor expression during macrophage differentiation is related to autophagy.

The neonatal Fc receptor (FcRn) plays a central role in recycling and biodistributing immunoglobulin G. FcRn is also involved in many physiological immune functions as well as pathological immune responses in cancer or autoimmune diseases. Low levels of FcRn in tumor cells and the microenvironment is associated with poor prognosis in non-small cell lung cancers. Among cells that are present in the tumor microenvironment, macrophages express high levels of FcRn. Macrophages are involved in these pathophysiological contexts by their dual differentiation states of pro- or anti-inflammatory macrophages. However, variations in FcRn protein expression have not been described in macrophage subtypes. In this work, we studied FcRn expression in an in vitro model of pro- and anti-inflammatory macrophage differentiation. We demonstrated an inverse relation between FcRn protein and mRNA expression in macrophage populations. Autophagy, which is involved in protein degradation and acquisition of phagocytic function in macrophages, participated in regulating FcRn levels. Intravenous immunoglobulin protected FcRn against autophagosome degradation in anti-inflammatory macrophages. Our data demonstrate that autophagy participates in regulating FcRn expression in pro- and anti-inflammatory macrophages. This finding raises new questions concerning the regulation of FcRn in immune functions.

Color Sparse Representations for Image Processing: Review, Models, and Prospects.

Sparse representations have been extended to deal with color images composed of three channels. A review of dictionary-learning-based sparse representations for color images is made here, detailing the differences between the models, and comparing their results on the real and simulated data. These models are considered in a unifying framework that is based on the degrees of freedom of the linear filtering/transformation of the color channels. Moreover, this allows it to be shown that the scalar quaternionic linear model is equivalent to constrained matrix-based color filtering, which highlights the filtering implicitly applied through this model. Based on this reformulation, the new color filtering model is introduced, using unconstrained filters. In this model, spatial morphologies of color images are encoded by atoms, and colors are encoded by color filters. Color variability is no longer captured in increasing the dictionary size, but with color filters, this gives an efficient color representation.