Classification of incomplete feature vectors by radial basis function networks

The paper describes the use of radial basis function neural networks with Gaussian basis functions to classify incomplete feature vectors. The method uses the fact that any marginal distribution of a Gaussian distribution can be determined from the mean vector and covariance matrix of the joint distribution.

Emerging horizons for tick-borne pathogens: from the ‘one pathogen–one disease’ vision to the pathobiome paradigm

Ticks as vectors of several notorious zoonotic pathogens, represent an important and increasing threat for human, animal health in Europe. Recent application of new technology revealed the complexity of the tick microbiome that might impact upon its vectorial capacity. Appreciation of these complex systems is expanding our vision of tick-borne pathogens leading us to evolve a more integrated view that embraces the “pathobiome” representing the pathogenic agent integrated within its abiotic and biotic environments. In this review, we will explore how this new vision will revolutionize our understanding of tick-borne diseases. We will discuss the implications in terms of research approach for the future in order to efficiently prevent and control the threat posed by ticks.