CD28 Exerts Protective and Detrimental Effects in a Pulmonary Model of Paracoccidioidomycosis

T-cell immunity has been claimed as the main immunoprotective mechanism against Paracoccidioides brasiliensis infection, the most important fungal infection in Latin America. As the initial events that control T-cell activation in paracoccidioidomycosis (PCM) are not well established, we decided to investigate the role of CD28, an important costimulatory molecule for the activation of effector and regulatory T cells, in the immunity against this pulmonary pathogen. Using CD28-deficient (CD28(-/-)) and normal wild-type (WT) C57BL/6 mice, we were able to demonstrate that CD28 costimulation determines in pulmonary paracoccidioidomycosis an early immunoprotection but a late deleterious effect associated with impaired immunity and uncontrolled fungal growth. Up to week 10 postinfection, CD28(-/-) mice presented increased pulmonary and hepatic fungal loads allied with diminished production of antibodies and pro-and anti-inflammatory cytokines besides impaired activation and migration of effector and regulatory T (Treg) cells to the lungs. Unexpectedly, CD28-sufficient mice progressively lost the control of fungal growth, resulting in an increased mortality associated with persistent presence of Treg cells, deactivation of inflammatory macrophages and T cells, prevalent presence of anti-inflammatory cytokines, elevated fungal burdens, and extensive hepatic lesions. As a whole, our findings suggest that CD28 is required for the early protective T-cell responses to P. brasiliensis infection, but it also induces the expansion of regulatory circuits that lately impair adaptive immunity, allowing uncontrolled fungal growth and overwhelming infection, which leads to precocious mortality of mice.

TLR2 Is a Negative Regulator of Th17 Cells and Tissue Pathology in a Pulmonary Model of Fungal Infection

To study the role of TLR2 in a experimental model of chronic pulmonary infection, TLR2-deficient and wild-type mice were intratracheally infected with Paracoccidioides brasiliensis, a primary fungal pathogen. Compared with control, TLR2(-/-) mice developed a less severe pulmonary infection and decreased NO synthesis. Equivalent results were detected with in vitro-infected macrophages. Unexpectedly, despite the differences in fungal loads both mouse strains showed equivalent survival times and severe pulmonary inflammatory reactions. Studies on lung-infiltrating leukocytes of TLR2(-/-) mice demonstrated an increased presence of polymorphonuclear neutrophils that control fungal loads but were associated with diminished numbers of activated CD4(+) and CD8(+) T lymphocytes. TLR2 deficiency leads to minor differences in the levels of pulmonary type 1 and type 2 cytokines, but results in increased production of KC, a CXC chemokine involved in neutrophils chemotaxis, as well as TGF-beta, IL-6, IL-23, and IL-17 skewing T cell immunity to a Th17 pattern. In addition, the preferential Th17 immunity of TLR2(-/-) mice was associated with impaired expansion of regulatory CD4(+)CD25(+)FoxP3(+) T cells. This is the first study to show that TLR2 activation controls innate and adaptive immunity to P. brasiliensis infection. TLR2 deficiency results in increased Th17 immunity associated with diminished expansion of regulatory T cells and increased lung pathology due to unrestrained inflammatory reactions. The Journal of Immunology, 2009, 183: 1279-1290.

MyD88 Signaling Is Required for Efficient Innate and Adaptive Immune Responses to Paracoccidioides brasiliensis Infection

The mechanisms that govern the initial interaction between Paracoccidioides brasiliensis, a primary dimorphic fungal pathogen, and cells of the innate immunity need to be clarified. Our previous studies showed that Toll-like receptor 2 (TLR2) and TLR4 regulate the initial interaction of fungal cells with macrophages and the pattern of adaptive immunity that further develops. The aim of the present investigation was to assess the role of MyD88, an adaptor molecule used by TLRs to activate genes of the inflammatory response in pulmonary paracoccidioidomycosis. Studies were performed with normal and MyD88(-/-) C57BL/6 mice intratracheally infected with P. brasiliensis yeast cells. MyD88(-/-) macrophages displayed impaired interaction with fungal yeast cells and produced low levels of IL-12, MCP-1, and nitric oxide, thus allowing increased fungal growth. Compared with wild-type (WT) mice, MyD88(-/-) mice developed a more severe infection of the lungs and had marked dissemination of fungal cells to the liver and spleen. MyD88(-/-) mice presented low levels of Th1, Th2, and Th17 cytokines, suppressed lymphoproliferation, and impaired influx of inflammatory cells to the lungs, and this group of cells comprised lower numbers of neutrophils, activated macrophages, and T cells. Nonorganized, coalescent granulomas, which contained high numbers of fungal cells, characterized the severe lesions of MyD88(-/-) mice; the lesions replaced extensive areas of several organs. Therefore, MyD88(-/-) mice were unable to control fungal growth and showed a significantly decreased survival time. In conclusion, our findings demonstrate that MyD88 signaling is important in the activation of fungicidal mechanisms and the induction of protective innate and adaptive immune responses against P. brasiliensis.

Selecting salient objects in real scenes: An oscillatory correlation model

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system. (C) 2010 Elsevier Ltd. All rights reserved.

Scalable model-based configuration management of security services in complex enterprise networks

Security administrators face the challenge of designing, deploying and maintaining a variety of configuration files related to security systems, especially in large-scale networks. These files have heterogeneous syntaxes and follow differing semantic concepts. Nevertheless, they are interdependent due to security services having to cooperate and their configuration to be consistent with each other, so that global security policies are completely and correctly enforced. To tackle this problem, our approach supports a comfortable definition of an abstract high-level security policy and provides an automated derivation of the desired configuration files. It is an extension of policy-based management and policy hierarchies, combining model-based management (MBM) with system modularization. MBM employs an object-oriented model of the managed system to obtain the details needed for automated policy refinement. The modularization into abstract subsystems (ASs) segment the system-and the model-into units which more closely encapsulate related system components and provide focused abstract views. As a result, scalability is achieved and even comprehensive IT systems can be modelled in a unified manner. The associated tool MoBaSeC (Model-Based-Service-Configuration) supports interactive graphical modelling, automated model analysis and policy refinement with the derivation of configuration files. We describe the MBM and AS approaches, outline the tool functions and exemplify their applications and results obtained. Copyright (C) 2010 John Wiley & Sons, Ltd.

Chaotic phase synchronization and desynchronization in an oscillator network for object selection

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial Visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

Three-dimensional, fully adaptive simulations of phase-field fluid models

We present an efficient numerical methodology for the 31) computation of incompressible multi-phase flows described by conservative phase-field models We focus here on the case of density matched fluids with different viscosity (Model H) The numerical method employs adaptive mesh refinements (AMR) in concert with an efficient semi-implicit time discretization strategy and a linear, multi-level multigrid to relax high order stability constraints and to capture the flow`s disparate scales at optimal cost. Only five linear solvers are needed per time-step. Moreover, all the adaptive methodology is constructed from scratch to allow a systematic investigation of the key aspects of AMR in a conservative, phase-field setting. We validate the method and demonstrate its capabilities and efficacy with important examples of drop deformation, Kelvin-Helmholtz instability, and flow-induced drop coalescence (C) 2010 Elsevier Inc. All rights reserved