Kuster T.M.,University of Manchester |
Kuster T.M.,Institute for Plant Production science |
Wilkinson A.,University of Manchester |
Hill P.W.,Bangor University |
And 2 more authors.
Plant and Soil | Year: 2016
Introduction: Grass species may acquire different forms of nitrogen (N) to reduce competition for the same resources. Climate change influences the availability of soil N and is therefore likely to cause shifts in N forms acquired by plants, thereby affecting their competitive interactions. Methods: We investigated the effects of warming on the uptake of different N forms and competitive interactions of Festuca ovina and Anthoxanthum odoratum in a pot experiment. The plants were grown either in monocultures or mixture, and at ambient or elevated temperature (+10 °C), and supplied with 13C and 15N isotopes to test for treatment effects on the relative uptake of ammonium, alanine or tri-alanine. Results: Both grass species took up relatively more N supplied as ammonium than as alanine or tri-alanine when grown under ambient conditions in monoculture. In contrast, when grown in mixtures, F. ovina took up the three supplied N forms in equal amounts, whereas A. odoratum switched to tri-alanine as the main N form. Under warmed conditions, both species took up the N forms equally, irrespective of competition treatments. Conclusions: We have shown that grass species grown in mixture and under ambient conditions reduce competition by acquiring different N forms. Warming increased the availability of inorganic N in the soil and therefore deregulated the need for differential uptake of N forms. © 2016 The Author(s)
Ancay A.,Institute for Plant Production science |
Carlen C.,Institute for Plant Production science
Acta Horticulturae | Year: 2016
To compare two training systems and three red currant cultivars ('Red Poll', 'Rovada', 'Tatran') a trial was carried out in Switzerland in Bruson at 1100 m a.s.l. The considered training system for red currant production were: i) the traditional cordon training system (palmette system) with 3 not removed cordons (main branches) and 15 to 20 medium sized lateral braches per plant and ii) the V-training system with constantly renewed cordons, i.e., cordons were removed after 3 years at their basis (no permanent wood) and every year, 3 new cordons were established in order to obtain 2- and 3-year-old cordons producing fruits (V-system). The results over 7 years revealed that the V-training system had little influence on yield, cluster attributes and fruit quality, but a very positive incidence on the picking efficiency with 11% more fruits harvested per hour and on training investments with 15% less training hours. The economic performance of the new V-system was in average of the three cultivars 13% better than the traditional cordon system (palmette). 'Rovada' and 'Tatran' were more productive than 'Red Poll', mainly due to a higher number of clusters per plant. The economic performance was best for 'Rovada' and 'Tatran' grown in V-system.
Qeli E.,University of Zurich |
Qeli E.,Swiss Reinsurance Company Ltd |
Omasits U.,University of Zurich |
Omasits U.,ETH Zurich |
And 9 more authors.
Journal of Proteomics | Year: 2014
The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. Biological significance: Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction of the best-suited PTPs for targeted proteomics applications. By building on methods developed in the field of information retrieval (e.g. web search engines like Google's PageRank), we circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the experimentalistD́s need for selecting e.g. the 5 most promising peptides for targeting a protein of interest. This approach allows to predict PTPs for not yet observed proteins or for organisms without prior experimental proteomics data such as many non-model organisms. © 2014 Elsevier B.V.
Verdenal T.,Institute for Plant Production science |
Spangenberg J.E.,University of Lausanne |
Zufferey V.,Institute for Plant Production science |
Lorenzini F.,Institute for Food science |
And 2 more authors.
Australian Journal of Grape and Wine Research | Year: 2015
Yeast assimilable nitrogen (YAN) in grape must is an important determinant of wine composition. The effect of foliar nitrogen fertilisation on YAN concentration in must of Vitis vinifera L. cv. Chasselas was studied. Nitrogen assimilation and translocation were investigated by applying 15N-labelled urea at flowering and at veraison. Methods and Results: Foliar urea was applied on field-grown Chasselas grapevines using labelled (10 atom% 15N) and unlabelled urea. The vines were excavated at harvest, and plant parts were separated and analysed. Thus, the distribution of dry organic matter and of total organic carbon and total organic nitrogen in the plant at harvest was determined. Bunches were the strongest N sink among all of the organs during both fertilisation periods. The highest YAN in the must, however, was obtained when the urea was applied during veraison. Conclusions: Isotope labelling was used to describe N partitioning throughout the vine in response to foliar nitrogen fertilisation with urea at flowering and at veraison. Differences between organs in carbon and nitrogen isotope discrimination at natural abundance were established. Fertilisation with urea during veraison increased the YAN concentration in Chasselas grape must. Significance of the Study: Results show that it is more effective to correct YAN deficiency in the vineyard with application of foliar urea during veraison than during flowering. © 2015 Australian Society of Viticulture and Oenology Inc.
Gilli C.,Institute for Plant Production science |
Sigg P.,Institute for Plant Production science |
Carlen C.,Institute for Plant Production science
Acta Horticulturae | Year: 2015
The production of pelargonium occurs between February and May, this period of the year is favourable to a temperature integration (TI) regime. From 2009 to 2011, trials were carried out in two identical, 90 m2-sized greenhouses, to evaluate the energy saving potential of the TI and to measure its effects on plants. Temperature integration is based on the plants' capacity to tolerate variations around an optimum temperature. The excess of solar energy during sunny days is compensated by decreasing consequently the night setpoint temperature. Pelargonium were grown either under a standard regime with fixed setpoints (Tnight: 12°C, Tday: 15°C, Tventilation: 18°C), or under a TI-regime (5°C