Role of IFN-gamma in immune responses to Candida albicans infections

Candida albicans is the most frequent etiologic agent that causes opportunistic fungal infections called candidiasis, a disease whose systemic manifestation could prove fatal and whose incidence is increasing as a result of an expanding immunocompromised population. Here we review the role of interferon-gamma (IFN-γ) in host protection against invasive candidiasis. This cytokine plays an essential role in both the innate and adaptive arms of the immune response to candidiasis. We focus on recent progress on host-pathogen interactions leading to the production of IFN-γ by host cells. IFN-γ is produced by CD4 Th1, CD8, γδ T, and natural killer (NK) cells, essentially in response to both IL-12 and/or IL-18; more recently, a subset of C. albicans-specific Th17 cells have been described to produce both IL-17 and IFN-γ. IFN-γ plays an important role in the regulation of the immune system as well as in the control of the infectious process, as it is required for optimal activation of phagocytes, collaborates in the generation of protective antibody response, and favors the development of a Th1 protective response.

Guidance of robot arms using depth data from RGB-D camera

Image Based Visual Servoing (IBVS) is a robotic control scheme based on vision. This scheme uses only the visual information obtained from a camera to guide a robot from any robot pose to a desired one. However, IBVS requires the estimation of different parameters that cannot be obtained directly from the image. These parameters range from the intrinsic camera parameters (which can be obtained from a previous camera calibration), to the measured distance on the optical axis between the camera and visual features, it is the depth. This paper presents a comparative study of the performance of D-IBVS estimating the depth from three different ways using a low cost RGB-D sensor like Kinect. The visual servoing system has been developed over ROS (Robot Operating System), which is a meta-operating system for robots. The experiments prove that the computation of the depth value for each visual feature improves the system performance.