Altered relaxation times in MRI indicate bronchopulmonary dysplasia.

We developed a MRI protocol using transverse (T2) and longitudinal (T1) mapping sequences to characterise lung structural changes in preterm infants with bronchopulmonary dysplasia (BPD). We prospectively enrolled 61 infants to perform 3-Tesla MRI of the lung in quiet sleep. Statistical analysis was performed using logistic Group Lasso regression and logistic regression.

Modeling of radon exhalation from soil influenced by environmental parameters.

Atmospheric radioactive noble gas radon (Rn-222) originates from soil gas exhaled in the atmospheric surface layer. Radon exhalation rates from soil as well as corresponding meteorological and soil parameters were recorded for two subsequent years. Based on long-term field data, a statistical regression model for the radon exhalation and the most important influencing parameters soil water content, temperature of soil and air, air pressure and autocorrelation of the exhalation rate was established.

Making complex prediction rules applicable for readers: Current practice in random forest literature and recommendations.

Ideally, prediction rules should be published in such a way that readers may apply them, for example, to make predictions for their own data. While this is straightforward for simple prediction rules, such as those based on the logistic regression model, this is much more difficult for complex prediction rules derived by machine learning tools. We conducted a survey of articles reporting prediction rules that were constructed using the random forest algorithm and published in PLOS ONE in 2014-2015 in the field "medical and health sciences", with the aim of identifying issues related to their applicability.