Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Is use of nifurtimox for the treatment of Chagas disease compatible with breast feeding? A population pharmacokinetics analysis.

Introduction Women with Chagas disease receiving treatment with nifurtimox are discouraged from breast feeding. Many patients who would receive treatment with nifurtimox live in extreme poverty, have limited access to resources such as clean water and baby formula and may not have safe alternatives to breast milk. Aim We aimed to estimate, using limited available pharmacokinetics data, potential infant exposure to nifurtimox through breast milk. Methods Original nifurtimox plasma concentrations were obtained from published studies. Pharmacokinetic parameters were estimated using non-linear mixed-effect modelling with NONMEM V.VI. A total of 1000 nifurtimox plasma-concentration profiles were simulated and used to calculate the amount of drug that an infant would be exposed to, if breast fed 150 ml/kg/day. Results Breast milk concentrations on the basis of peak plasma levels (1361 ng/ml) and milk-plasma ratio were estimated. We calculated infant nifurtimox exposure of a breastfed infant of a mother treated with this drug to be below 10% of the maternal weight-adjusted dose, even if milk-plasma ratio were overestimated. Simulation led to similar estimates. Discussion Risk for significant infant exposure to nifurtimox through breast milk seems small and below the level of exposure of infants with Chagas disease receiving nifurtimox treatment. This potential degree of exposure may not justify discontinuation of breast feeding.