Sonic hedgehog-responsive lipoxygenases and cyclooxygenase-2 modulate Dectin-1-induced inflammatory cytokines

Immune responses during fungal infections are predominately mediated by 5/15-lipoxygenases (LO)-or cyclooxygenase (COX)-2-catalysed bioactive eicosanoid metabolites like leukotrienes, lipoxins and prostaglandins. Although few host mediators of fungi-triggered eicosanoid production have been established, the molecular mechanism of expression and regulation of 5-LO, 15-LO and COX-2 are not well-defined. Here, we demonstrate that, macrophages infected with representative fungi Candida albicans, Aspergillus flavus or Aspergillus fumigatus or those treated with Curdlan, a selective agonist of pattern recognition receptor for fungi Dectin-1, displays increased expression of 5-LO, 15-LO and COX-2. Interestingly, Dectin-1-responsive Syk pathway activates mTOR-sonic hedgehog (SHH) signaling cascade to stimulate the expression of these lipid metabolizing enzymes. Loss-of-function analysis of the identified intermediaries indicates that while Syk-mTOR-SHH pathway-induced 5-LO and 15-LO suppressed the Dectin-l-responsive pro-inflammatory signature cytokines like TNE-alpha, IL-1 beta and IL-12, Syk-mTOR-SHH-induced COX-2 positively regulated these cytokines. Dectin-1-stimulated IL-6, however, is dependent on 5-LO, 15-LO and COX-2 activity. Together, the current study establishes Dectin-1-arbitrated host mediators that direct the differential regulation of immune responses during fungal infections and thus are potential candidates of therapeutic intervention. (C) 2015 Elsevier Ltd. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.