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Biogeochemical models are useful for the prediction of nitrogen (N) cycling processes, but accurate description of the denitrification and decomposition sub-modules is critical. Current models were developed before suitable soil N2 flux data were available; new measurement techniques have enabled the collection of improved N2 data. We use measured data from two laboratory incubations to test the denitrification sub-modules of existing biogeochemical models. Two arable soils -- a silt-loam and a sand -- were incubated for 34 and 58 days, respectively. Fluxes of N2, N2O and CO2 were quantified using gas chromatography and isotope-ratio mass spectrometry (IRMS). For the loamy soil, seven moisture and three NO3- contents were included, with temperature changing during the incubation. The sandy soil was incubated with and without incorporation of litter (ryegrass), with temperature, water content and NO3- content changing during the incubation. Three common biogeochemical models (Coup, DNDC and DeNi) were tested using the data. No systematic calibration of the model parameters was conducted since our intention was to evaluate the general model structure or 'default' model runs. As compared with measured fluxes, the average N2+N2O fluxes of the default runs for loamy soil were approximately 3 times higher for Deni, 105 times smaller for DNDC and 22 times smaller for Coup. For the sandy soils, default runs were 3 times higher for DeNi, 7 times smaller for DNDC and 12 times smaller for Coup. While measured fluxes were overestimated by DeNi and underestimated by DNDC and Coup, the temporal patterns of the measured and the modeled emissions were similar for the different treatments. None of the models was able to determine litter-induced decomposition correctly. The reason for the differences between the measured and modeled values can be traced back to model structure uncertainty and/or parameter uncertainty. Given the aim of our work -- to assess existing model processes for further development and/or to identify missing processes within the models -- these results provide valuable insights into avenues for future research. We conclude that the predicting power of the models could be improved through future experiments that collect data on denitrification activity with a concurrent focus on control parameter determination. [ABSTRACT FROM AUTHOR]

Nitrous oxide emissions from a network of agricultural experiments in Europe and Zimbabwe were used to explore the relative importance of site and management controls of emissions. At each site, a selection of management interventions were compared within replicated experimental designs in plot based experiments. Arable experiments were conducted at Beano in Italy, El Encin in Spain, Foulum in Denmark, Logården in Sweden, Maulde in Belgium, Paulinenaue in Germany, Harare in Zimbabwe and Tulloch in the UK. Grassland experiments were conducted at Crichton, Nafferton and Peaknaze in the UK, Gödöllö in Hungary, Rzecin in Poland, Zarnekow in Germany and Theix in France. Nitrous oxide emissions were measured at each site over a period of at least two years using static chambers. Emissions varied widely between sites and as a result of manipulation treatments. Average site emissions (throughout the study period) varied between 0.04 and 21.21 kg N2O-Nha-1 yr-1, with the largest fluxes and variability associated with the grassland sites. Total nitrogen addition was found to be the single most important determinant of emissions, accounting for 15% of the variance (using linear regression) in the data from the arable sites (p < 0.0001), and 77% in the grassland sites. The annual emissions from arable sites were significantly greater than those that would be predicted by IPCC default emission factors. Variability in N2O within sites that occurred as a result of manipulation treatments was greater than that resulting from site to site and year to year variation, highlighting the importance of management interventions in contributing to greenhouse gas mitigation. [ABSTRACT FROM AUTHOR]

Static chamber measurements of nitrous oxide (N2O) and methane (CH4) fluxes were made from five characteristic vegetation types, representing three different natural ecosystems (grasslands, deciduous forest and wetlands) in the Pannonian basin, Hungary. The main objective of the study was to determine the drivers of average seasonal, annual and interannual N2O and CH4 fluxes in these different ecosystems to enable more accurate predictions of responses to future climatic conditions. Investigations into the response of net N2O and CH4 emission rates to soil temperature and soil water content were carried out over a 2-year period. Both N2O and CH4 fluxes covered a wide range. Yearly average N2O emissions ranged from 4.38 mg N m−2 year−1 for wetland poplar forest to 242 mg N m−2 year−1 for mountain oak forest. Yearly average soil fluxes of CH4 varied from oxidation, −106 mg CH4 m−2 year−1, for loess grassland to an emission of 129 mg CH4 m−2 year−1 for a wetland Glyceria stand. Multiple regression analyses showed that N2O fluxes from the Pannonian grasslands and oak forest were more dependent than CH4 fluxes on the key soil variables water content and temperature. The largest seasonal mean N2O emission, 319 mg N m−2 year−1, from a mountain oak forest, was observed in summer, and the largest seasonal mean CH4 emission, equivalent to an annual rate of 423 mg CH4 m−2 year−1, from a wetland Glyceria stand, was found in the spring of the wet year. [ABSTRACT FROM AUTHOR]

Many patients with neuropathies, ataxias, dystonias and other neurological diseases remain without a genetic diagnosis following gold standard diagnostic testing. Lack of a diagnosis could be because new disease genes need to be found, or because diagnostic methods fail to detect challenging variants in known disease genes. It could also be because research into adult-onset genetic diseases, which many of these diseases are, has been relatively neglected compared to early-onset diseases. Our trans-Australia collaboration between Perth, Sydney, Melbourne, Brisbane and Adelaide is analysing DNA from patients with peripheral motor and sensory neuropathies, ataxias, dystonia, hereditary spastic paraplegia etc. who have not received a molecular diagnosis following testing in Australian or overseas diagnostic laboratories, with the aim of identifying the molecular diagnosis. We are using short-read and long-read genome sequencing and optical genome mapping allied to new bioinformatic tools including the T2T and pangenome genomes. Our collaborating laboratories have multiple model systems in which to model candidate variants. To date we have demonstrated that long-read sequencing can overcome issues with pseudogenes (SORD) and clarify pathogenic structural variants. We have identified deep intronic variants causing peripheral neuropathy (MME), participated in the identification of FGF14 STR ataxia (SCA27B), further clarified the genetics of RFC1 ataxia and peripheral neuropathy, and developed new tools for targeted long-read sequencing and short-tandem repeat detection. We have used C. elegans modelling to analyse riboflavin transport deficiency and are modelling additional disorders in zebrafish. We have completed two reviews: one proposing methods to identify the remaining hidden genetics of inherited peripheral neuropathy and one on the use of iPSCs in analysing neurogenic disorders. Together, our collaboration has led to numerous diagnoses for Australian patients. [ABSTRACT FROM AUTHOR]

Rationale: Field measurement of denitrification in agricultural ecosystems using the 15 N gas flux method has been limited by poor sensitivity because current isotope ratio mass spectrometry is not precise enough to detect low 15 N 2 fluxes in the presence of a high atmospheric N 2 background. For laboratory studies, detection limits are improved by incubating soils in closed systems and under N 2 -depleted atmospheres., Methods: We developed a new procedure to conduct the 15 N gas flux method suitable for field application using an artificially N 2 -depleted atmosphere to improve the detection limit at the given precision of mass spectrometry. Laboratory experiments with and without 15 N-labelling and using different flushing strategies were conducted to develop a suitable field method. Subsequently, this method was tested in the field and results were compared with those obtained from the conventional 15 N gas flux method., Results: Results of the two methods were in close agreement showing that the denitrification rates determined were not biased by the flushing procedure. Best sensitivity for N 2  + N 2 O fluxes was 10 ppb, which was 80-fold better than that of the reference method. Further improvement can be achieved by lowering the N 2 background concentration below the values established in the present study., Conclusions: In view of this progress in sensitivity, the new method will be suitable to measure denitrification dynamics in the field beyond peak events., (© 2018 John Wiley & Sons, Ltd.)

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

The authors describe an unusually case of throat tumor and dyspnea in course of Elongated Styloid Process Syndrome. They pay attention to X-ray examination, including CT, in correct diagnosis.