G protein-coupled oestrogen receptor 1, oestrogen receptors and androgen receptor in the sand rat (Psammomys obesus) efferent ducts

Background: The efferent ducts are mainly involved in the reabsorption of the seminiferous tubular fluid. Testosterone and oestrogens regulate efferent ducts functions via their receptors.Materials and methods: This paper presents an experimental investigation on the location of the P450 aromatase, the 17-b oestradiol (E2), the androgen receptor (AR), the oestrogen receptor 1 (ESR1), the oestrogen receptor 2 (ESR2) and the G protein-coupled oestrogen receptor 1 (GPER1) in the efferent ducts using Psammomys obesus as an animal model to highlight the effect of the season on the histology and the distribution of these receptors.Results: We observed a proliferation of the connective tissue, decreasing in the height of the epithelium during the resting season compared to the breeding season. Ciliated cells expressed P450 aromatase, AR, E2, ESR1, ESR2 and GPER1 during both seasons. Basal cells showed a positive staining for the ESR1 and the GPER1 during both season, the AR and E2 during the breeding season and ESR2 during the resting season.Conclusions: Our result shows that the expression of androgen receptor and oestrogen receptors in the efferent ducts vary by season witch suggest that they are largely involved in the regulation of the efferent ducts functions

Reduction of predictive uncertainty in estimating irrigation water requirement through multi-model ensembles and ensemble averaging

Irrigation agriculture plays an increasingly important role in food supply. Many evapotranspiration models are used today to estimate the water demand for irrigation. They consider different stages of crop growth by empirical crop coefficients to adapt evapotranspiration throughout the vegetation period. We investigate the importance of the model structural versus model parametric uncertainty for irrigation simulations by considering six evapotranspiration models and five crop coefficient sets to estimate irrigation water requirements for growing wheat in the Murray–Darling Basin, Australia. The study is carried out using the spatial decision support system SPARE:WATER. We find that structural model uncertainty among reference ET is far more important than model parametric uncertainty introduced by crop coefficients. These crop coefficients are used to estimate irrigation water requirement following the single crop coefficient approach. Using the reliability ensemble averaging (REA) technique, we are able to reduce the overall predictive model uncertainty by more than 10%. The exceedance probability curve of irrigation water requirements shows that a certain threshold, e.g. an irrigation water limit due to water right of 400 mm, would be less frequently exceeded in case of the REA ensemble average (45%) in comparison to the equally weighted ensemble average (66%). We conclude that multi-model ensemble predictions and sophisticated model averaging techniques are helpful in predicting irrigation demand and provide relevant information for decision making

THE PGR NETWORKS IN FRANCE: COLLABORATION OF USERS AND THE GENETIC RESOURCE CENTRE ON SMALL GRAIN CEREALS

Plant genetic resources (PGR) have been used in breeding programs for many decades to produce modern varieties by introducing genes of interest, in particular, resistance genes. Nevertheless, these resources remain underestimated if we focus on abiotic stress tolerance or new agricultural techniques, which consider productivity with regard to the environment. In recent years, new users, such as scientists and farmers, have discovered diverse sources of interest for screening and exploiting natural diversity conserved in PGR collections.In the case of the French cereals PGR Network, a share of the responsibility, based on the knowledge and ability of network members, has been decided in order to better promote the use of PGR. The main species of Triticum (wheat), Hordeum (barley), Secale (rye), ×Triticosecale (triticale), Avena (oat) genera and their wild relatives are held in the collection. By combining phenotypic and genotypic data, the whole genetic resource collection has been structured into smaller functional groups of accessions, in order to facilitate the access and meet the increasing number of different requirements for the distribution of adapted samples of accessions.New panels are being processed to give breeders and scientists new useful tools to study, for instance, stress resistance or to develop association studies. All these data obtained from the French small grain cereal Network will be progressively available through the INRA Genetic Resource Website (http://urgi.versailles.inra.fr/siregal/siregal/welcome.do)

Spatial distribution of soils determines export of nitrogen and dissolved organic carbon from an intensively managed agricultural landscape

The surrounding landscape of a stream has crucial impacts on the aquatic environment. This study pictures the hydro-biogeochemical situation of the Tyrebækken creek catchment in central Jutland, Denmark. The intensively managed agricultural landscape is dominated by rotational croplands. The small catchment mainly consist of sandy soil types besides organic soils along the streams. The aim of the study was to characterise the relative influence of soil type and land use on stream water quality. Nine snapshot sampling campaigns were undertaken during the growing season of 2009. Total dissolved nitrogen (TDN), nitrate (NO3-), ammonium nitrogen and dissolved organic carbon (DOC) concentrations were measured, and dissolved organic nitrogen (DON) was calculated for each grabbed sample. Electrical conductivity, pH and flow velocity were measured during sampling. Statistical analyses showed significant differences between the northern, southern and converged stream parts, especially for NO3- concentrations with average values between 1.4 mg N l-1 and 9.6 mg N l-1. Furthermore, throughout the sampling period DON concentrations increased to 2.8 mg N l-1 in the northern stream contributing up to 81% to TDN. Multiple-linear regression analyses performed between chemical data and landscape characteristics showed a significant negative influence of organic soils on instream N concentrations and corresponding losses in spite of their overall minor share of the agricultural land (12.9%). On the other hand, organic soil frequency was positively correlated to the corresponding DOC concentrations. Croplands also had a significant influence but with weaker correlations. For our case study we conclude that the fractions of coarse textured and organic soils have a major influence on N and DOC export in this intensively used landscape. Meanwhile, the contribution of DON to the total N losses was substantial

Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four Dimensional Variational data assimilation

Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, prior or “background” errors (in the initial state and parameter values) and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a Four-Dimensional Variational data assimilation (4D-Var) scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation errors. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we assimilate a single year of NEE observations and then run a forecast for the next 14 years. We compare the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We find that using the new correlated matrices reduces the root mean square error in the 14 year forecast of daily NEE by 44% decreasing from 4.22 gCm−2 day−1 to 2.38 gCm−2 day−

Multi-model data fusion as a tool for PUB: example in a Swedish mesoscale catchment

Post-processing the output of different rainfall-runoff models allows one to pool strengths of each model to produce more reliable predictions. As a new approach in the frame of the "Prediction in Ungauged Basins" initiative, this study investigates the geographical transferability of different parameter sets and data-fusion methods which were applied to 5 different rainfall-runoff models for a low-land catchment in Central Sweden. After usual calibration, we adopted a proxy-basin validation approach between two similar but non-nested sub-catchments in order to simulate ungauged conditions. Many model combinations outperformed the best single model predictions with improvements of efficiencies from 0.70 for the best single model predictions to 0.77 for the best ensemble predictions. However no "best" data-fusion method could be determined as similar performances were obtained with different merging schemes. In general, poorer model performance, i.e. lower efficiency, was less likely to occur for ensembles which included more individual models

Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment

Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a self-developed tool, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale River Fyris catchment in mid-eastern Sweden. Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE) ranging between 0.48 and 0.83. Using the calibrated hydrological parameter sets, the parameter uncertainty linked to the nitrogen parameters was explored in order to cover the range of possible predictions of exported loads for 3 nitrogen species: nitrate (NO3), ammonium (NH4) and total nitrogen (Tot-N). For each model and each nitrogen species, predictions were ranked in two different ways according to the performance indicated by two different goodness-of-fit measures: the coefficient of determination R2 and the root mean square error RMSE. A total of 2160 deterministic Single Model Ensembles (SME) was generated using an increasing number of members (from the 2 best to the 10 best single predictions). Finally the best SME for each model, nitrogen species and discharge station were selected and merged into 330 different Multi-Model Ensembles (MME). The evolution of changes in R2 and RMSE was used as a performance descriptor of the ensemble procedure. In each studied case, numerous ensemble merging schemes were identified which outperformed any of their members. Improvement rates were generally higher when worse members were introduced. The highest improvements were achieved for the nitrogen SMEs compiled with multiple linear regression models with R2 selected members, which resulted in the RMSE decreasing by up to 90%

SSR allelic diversity changes in 480 European bread wheat varieties released from 1840 to 2000

A sample of 480 bread wheat varieties originating from 15 European geographical areas and released from 1840 to 2000 were analysed with a set of 39 microsatellite markers. The total number of alleles ranged from 4 to 40, with an average of 16.4 alleles per locus. When seven successive periods of release were considered, the total number of alleles was quite stable until the 1960s, from which time it regularly decreased. Clustering analysis on Nei's distance matrix between these seven temporal groups showed a clear separation between groups of varieties registered before and after 1970. Analysis of qualitative variation over time in allelic composition of the accessions indicated that, on average, the more recent the European varieties, the more similar they were to each other. However, European accessions appear to be more differentiated as a function of their geographical origin than of their registration period. On average, western European countries (France, The Netherlands, Great Britain, Belgium) displayed a lower number of alleles than southeastern European countries (former Yugoslavia, Greece, Bulgaria, Romania, Hungary) and than the Mediterranean area (Italy, Spain and Portugal), which had a higher number. A hierarchical tree on Nei's distance matrix between the 15 geographical groups of accessions exhibited clear opposition between the geographical areas north and south of the arc formed by the Alps and the Carpathian mountains. These results suggest that diversity in European wheat accessions is not randomly distributed but can be explained both by temporal and geographical variation trends linked to breeding practices and agriculture policies in different countries

Corrosion behaviour in saline solution of pulsed-electrodeposited zinc-nickel-ceria nanocomposite coatings

cited By 0International audiencePure Zn-Ni and Zn–Ni–ceria (CeO2) nanocomposite coatings were deposited onto steel substrates using pulse electrodeposition process from an acidic bath combined with preliminary sonication. Influences of different parameters such as pulse parameters, addition of nanoparticles in the electroplating bath, use of sonication to ensure their dispersion, were studied in terms of morphology, composition, and corrosion behaviour in saline solution. Results revealed a strong influence of the electrodeposition parameters on the corrosion behaviour of the Zn-Ni coatings. Incorporation of ceria nanoparticles is enhanced for the very short duration of the pulse due to the refinement of the microstructure. It was proved that composite coatings present an enhanced corrosion behaviour, while sonification does not afford a further improvement. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Using multi-model averaging to improve the reliability of catchment scale nitrogen predictions

Hydro-biogeochemical models are used to foresee the impact of mitigation measures on water quality. Usually, scenario-based studies rely on single model applications. This is done in spite of the widely acknowledged advantage of ensemble approaches to cope with structural model uncertainty issues. As an attempt to demonstrate the reliability of such multi-model efforts in the hydro-biogeochemical context, this methodological contribution proposes an adaptation of the reliability ensemble averaging (REA) philosophy to nitrogen losses predictions. A total of 4 models are used to predict the total nitrogen (TN) losses from the well-monitored Ellen Brook catchment in Western Australia. Simulations include re-predictions of current conditions and a set of straightforward management changes targeting fertilisation scenarios. Results show that, in spite of good calibration metrics, one of the models provides a very different response to management changes. This behaviour leads the simple average of the ensemble members to also predict reductions in TN export that are not in agreement with the other models. However, considering the convergence of model predictions in the more sophisticated REA approach assigns more weight to previously less well-calibrated models that are more in agreement with each other. This method also avoids having to disqualify any of the ensemble members