This text presents the key concepts of the TRANSGANG project in the form of answers to seven research questions: what, when, who, why, where, how and what for. We start by defining the conceptual triangle configured by the title: Transnational Gangs as agents of Mediation (What). The central chapters give the historical context: Neoliberal States (When); the central study subjects: Gangs (Who); the proactive approach: Mediation (Why); the border spaces to be researched in the three regions: the Americas, North Africa and Southern Europe (Where); and the methodological perspective adopted (How). The final concluding chapter explores the expected impact of the research, from resistance to resilience through empowerment (What For). The text includes a complete list of related literature ordered by topics and regions, and a specific Glossary.
Streptococcus pyogenes or Group A Streptococcus (GAS) is the etiologic agent of important human infections such as acute pharyngitis, impetigo, rheumatic fever and the streptococcal toxic shock syndrome. Binding of the complement regulatory proteins factor H, factor H-like protein 1 (FHL-1), C4b-binding protein (C4BP), or CD46 is a crucial step in the pathogenesis of these infections. M protein is the GAS protein that generally mediates these interactions. However, a detailed analysis of the reports that have investigated the binding of complement regulatory components to GAS indicates that this microorganism has evolved alternative mechanisms for the recruitment of complement regulatory proteins to the bacterial surface. This article summarizes these data to provide a starting point for future research aimed at the characterization of additional mechanisms developed by GAS to evade the immune system.
The complement regulatory protein CD46 (MCP, membrane cofactor protein) is used as a cell receptor by a number of bacterial and viral pathogens, including Streptococcus pyogenes (Group A Streptococci). The highly variable M (Emm) proteins are virulence factors of S. pyogenes, and Emm proteins of serotypes 5, 6 or 22 are able of binding to CD46, thus mediating the binding of Streptococci to human cells. In this work, using a soluble construction encompassing the extracellular domain of human CD46, we have analyzed its binding to clinical isolates of S. pyogenes, including isolates of the M types 1, 3 and 18 that are frequently found in invasive infections or rheumatic fever. Our data show a strong binding of CD46 to bacteria of M types 1, 3, 8, 18, 24, 28, 29, 31 and 78; weak binding to M6 and M29 and no binding to M types 11, 12, M27 or M30. Surprisingly, CD46 bound to isogenic mutants of one clinical M18 isolate lacking the Emm protein or Emm and the Emm-related protein Enn, regardless of having capsule or not. In addition, these isogenic mutants bound to keratinocytes in a CD46-dependent manner, confirming the role of CD46 as one of the cell receptors for Group A Streptococci. Furthermore, CD46 did not bind to a recombinant Emm 18 construct, confirming that Emm is not involved in CD46 binding to M18 bacteria. Emm-dependent and -independent CD46 binding of clinical isolates of Streptococci confirms the importance of CD46 as a cell target that might confer pathogens some biological advantages over the host.
This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling
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