ESPR uroradiology task force imaging recommendations in paediatric uroradiology, part VII: standardised terminology, impact of existing recommendations, and update on contrast-enhanced ultrasound of the paediatric urogenital tract

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Correction to: Plea for a standardized imaging approach to disorders of sex development in neonates: Consensus proposal from European Society of Paediatric Radiology task force

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Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Results of systematic screening for minor degrees of fetal renal pelvis dilatation in an unselected population

OBJECTIVE: The purpose of this study was to determine the incidence of minor degrees of renal pelvis dilatation that is detected by antenatal ultrasound scanning in an unselected population and its value in the prediction of significant uropathies. STUDY DESIGN: This prospective study was conducted over a 24-month period. Infants with an anteroposterior pelvic diameter of ≥4 mm in the second trimester and/or ≥7 mm but <15 mm in the third trimester were enrolled. RESULTS: Pyelectasis was found in 4.5% of 5643 fetuses (1.5% with significant uropathy). Among the 213 infants whose cases were followed, 132 infants (62%) had renal anomalies, but only 83 infants (39%) had significant uropathies. The ability of the third-trimester renal pelvis dilatation to predict renal abnormalities showed a positive predictive value of 69%. Pyelectasis that was detected only in the second trimester revealed a significant uropathy in 12% of the infants. CONCLUSION: Pyelectasis was found in 4.5% of fetuses. The third-trimester anteroposterior renal pelvis diameter of ≥7 mm was the best ultrasound criterion to predict postnatal uropathies.SCOPUS: ar.jinfo:eu-repo/semantics/publishe