Cryptococcosis in Atlántico, Colombia: an approximation of the prevalence of this mycosis and the distribution of the etiological agent in the environment

ABSTRACTINTRODUCTION:Cryptococcosis is an invasive disease acquired by inhalation of infectious propagules from the environment. Currently, compulsory notification of the spread of this disease is not required in Colombia. However, reporting of human immunodeficiency virus (HIV)/acquired immune deficiency syndrome cases to the National Surveillance System has suggested that there is a growing population at risk of contracting cryptococcosis. Few studies have described the occurrence of cryptococcosis in Colombia. Therefore, in this study, we examined the pathology of this disease in Atlántico, Colombia and determined the distributions of Cryptococcus neoformans and Cryptococcus gattii in the environment.METHODS:Clinical samples/isolates were gathered from cases of cryptococcosis previously diagnosed at health institutions in Atlántico, and surveys were completed by clinicians. The environmental study considered 32 sampling points and three tree species, i.e., Quickstick ( Gliricidia sepium ), Almond ( Terminalia catappa ), and Pink trumpet ( Tabebuia rosea ). Environmental and clinical samples/isolates were analyzed for phenotypic and genotypic confirmation.RESULTS:From 1997-2014, 41 cases of cryptococcosis were reported. The mean patient age was 40.5 years (range: 18-63 years); 76% were men, and 78% were HIV positive. Isolation was possible in 38 cases ( C. neoformans , molecular type VNI in 37 cases and C. gattii , molecular type VGI in one case). In 2012-2014, 2,068 environmental samples were analyzed with a positivity of 0.4% ( C. neoformans , molecular type VNI) in Almond and Pink trumpet trees.CONCLUSIONS:Cryptococcus neoformans , molecular type VNI had a higher prevalence than C. gattii and was associated with human exposure and the pathogenesis of cryptococcosis in this geographical region.

Algorithm to determine the intersection curves between bezier surfaces by the solution of multivariable polynomial system and the differential marching method

The determination of the intersection curve between Bézier Surfaces may be seen as the composition of two separated problems: determining initial points and tracing the intersection curve from these points. The Bézier Surface is represented by a parametric function (polynomial with two variables) that maps a point in the tridimensional space from the bidimensional parametric space. In this article, it is proposed an algorithm to determine the initial points of the intersection curve of Bézier Surfaces, based on the solution of polynomial systems with the Projected Polyhedral Method, followed by a method for tracing the intersection curves (Marching Method with differential equations). In order to allow the use of the Projected Polyhedral Method, the equations of the system must be represented in terms of the Bernstein basis, and towards this goal it is proposed a robust and reliable algorithm to exactly transform a multivariable polynomial in terms of power basis to a polynomial written in terms of Bernstein basis .

Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals

Non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. A recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. For a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (FIR) neural networks. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. The present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. The training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. Application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. The approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of Mach numbers in the transonic regime.