An analysis of precipitation isotope distributions across Namibia using historical data

Global precipitation isoscapes based on the Global Network for Isotopes in Precipitation (GNIP) network are an important toolset that aid our understanding of global hydrologiccycles. Although the GNIP database is instrumental in developing global isoscapes, data coverage in some regions of hydrological interest (e.g., drylands) is low or non-existent thus the accuracy and relevance of global isoscapes to these regions is debatable. Capitalizing on existing literature isotope data, we generated rainfall isoscapes for Namibia (dryland) using the cokriging method and compared it to a globally fitted isoscape (GFI) downscaled to country level. Results showed weak correlation between observed and predicted isotope values in the GFI model (r2 < 0.20) while the cokriging isoscape showed stronger correlation (r2 = 0.67). The general trend of the local cokriging isoscape is consistent with synoptic weather systems (i.e., influences from Atlantic Ocean maritime vapour, Indian Ocean maritime vapour, Zaire Air Boundary, the Intertropical Convergence Zone and Tropical Temperate Troughs) and topography affecting the region. However, because we used the unweighted approach in this method, due to data scarcity, the absolute values could beimproved in future studies. A comparison of local meteoric water lines (LMWL) constructed from the cokriging and GFI suggested that the GFI model still reflects the global averageeven when downscaled. The cokriging LMWL was however more consistent with expectations for an arid environment. The results indicate that although not ideal, for data deficientregions such as many drylands, the unweighted cokriging approach using historical local data can be an alternative approach to modelling rainfall isoscapes that are more relevantto the local conditions compared to using downscaled global isoscapes

Visualization of proteomics data using R and bioconductor.

Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.LG was supported by the European Union 7th Framework Program (PRIME-XS project, grant agreement number 262067) and a BBSRC Strategic Longer and Larger grant (Award BB/L002817/1). LMB was supported by a BBSRC Tools and Resources Development Fund (Award BB/K00137X/1). TN was supported by a ERASMUS Placement scholarship.This is the final published version of the article. It was originally published in Proteomics (PROTEOMICS Special Issue: Proteomics Data Visualisation Volume 15, Issue 8, pages 1375–1389, April 2015. DOI: 10.1002/pmic.201400392). The final version is available at http://onlinelibrary.wiley.com/doi/10.1002/pmic.201400392/abstract