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Boosting the productivity of smallholder farming systems continues to be a major need in Africa. Challenges relating to how to improve irrigation are multi-factor and multisectora l, and they involve a broad range of actors who must interact to reach decisions collectively. We provide a systematic reflection on findings from the research project EAU4Food, which adopted a transdisciplinary approach to irrigation for food security research in five case studies in Ethiopia, Mali, Mozambique, South Africa and Tunisia. The EAU4Food experiences emphasize that actual innovation at irrigated smallholder farm level remains limited without sufficient improvement of the enabling environment and taking note of the wider political economy environment. Most project partners felt at the end of the project that the transdisciplinary approach has indeed enriched the research process by providing different and multiple insights from actors outside the academic field. Local capacity to facilitate transdisciplinary research and engag ement with practitio ners was developed and could support the continuation and scaling up of the approach. Future projects may benefit from a longer time frame to allow for deeper exchange of lessons learned among different stakeholders and a dedicated effort to analyse possible improvements of the enabling environment from the beginning of the research process.

Glacial isostatic adjustment (GIA) is the response of the solid Earth to past ice loading, primarily, since the Last Glacial Maximum, about 20 K yr BP. Modelling GIA is challengingbecause of large uncertainties in ice loading history and also the viscosity of the upper and lower mantle. GPS data contain the signature of GIA in their uplift rates but these also contain other sources of vertical land motion (VLM) such as tectonics, human and natural influences on water storage that can mask the underlying GIA signal. In this study, we use about 4000GPS vertical velocities as observational estimates of global GIA uplift rates, after correcting for major elastic deformation effects. A novel fully automatic strategy is developed to postprocess the GPS time-series and to correct for non-GIA artefacts. Before estimating verticalvelocities and uncertainties, we detect outliers and jumps and correct for atmospheric massloading displacements. We correct the resulting velocities for the elastic response of the solid Earth to global changes in ice sheets, glaciers and ocean loading, as well as for changes in the Earth’s rotational pole relative to the 20th century average. We then apply a spatial median filter to remove sites where local effects are dominant to leave approximately 4000 GPS sites. The resulting novel global GPS data set shows a clean GIA signal at all post-processed stations and is therefore suitable to investigate the behavior of global GIA forward models.The results are transformed from a frame with its origin in the centre of mass of the total Earth’s system (CM) into a frame with its origin in the centre of mass of the solid Earth (CE) before comparison with 13 global GIA forward model solutions, with best fits with Pur-6-VM5 and ICE-6G predictions. The largest discrepancies for all models were identified for Antarctica and Greenland, which may be due to either uncertain mantle rheology, ice loading history/magnitude and/or GPS errors.

The Antarctic Ice Sheet is the largest potential source of future sea-level rise. Mass loss has been increasing over the last 2 decades for the West Antarctic Ice Sheet (WAIS) but with significant discrepancies between estimates, especially for the Antarctic Peninsula. Most of these estimates utilise geophysical models to explicitly correct the observations for (unobserved) processes. Systematic errors in these models introduce biases in the results which are difficult to quantify. In this study, we provide a statistically rigorous error-bounded trend estimate of ice mass loss over the WAIS from 2003 to 2009 which is almost entirely data driven. Using altimetry, gravimetry, and GPS data in a hierarchical Bayesian framework, we derive spatial fields for ice mass change, surface mass balance, and glacial isostatic adjustment (GIA) without relying explicitly on forward models. The approach we use separates mass and height change contributions from different processes, reproducing spatial features found in, for example, regional climate and GIA forward models, and provides an independent estimate which can be used to validate and test the models. In addition, spatial error estimates are derived for each field. The mass loss estimates we obtain are smaller than some recent results, with a time-averaged mean rate of −76 ± 15 Gt yr−1 for the WAIS and Antarctic Peninsula, including the major Antarctic islands. The GIA estimate compares well with results obtained from recent forward models (IJ05-R2) and inverse methods (AGE-1). The Bayesian framework is sufficiently flexible that it can, eventually, be used for the whole of Antarctica, be adapted for other ice sheets and utilise data from other sources such as ice cores, accumulation radar data, and other measurements that contain information about any of the processes that are solved for.

Chapter 2 focused entirely on aleatory uncertainty. This is the uncertainty that arises out of the randomness of the hazard itself, and also, possibly, out of the responses to the hazard outcome. That chapter chased this uncertainty through the footprint function and a loss operator to arrive at an exceedance probability (EP) curve. Such a structured approach (e.g. as opposed to a purely statistical approach) was motivated by the need to evaluate different interventions for choosing between different actions; and by the possibility of non-stationarity in the boundary conditions on policy-relevant timescales measured in decades. Different risk managers will have different loss operators, and hence different EP curves. Likewise, the same risk manager will have different EP curves for different actions. A very simple summary statistic of an EP curve is the area underneath it, which corresponds to the expected loss (‘expectation’ taken in the mathematical sense), which is defined to be the risk.

In the present study, we report the first pathogenic mutation in the CACNA2D1 gene in humans, which causes a new variant of SQTS. It remains to be determined whether mutations in this gene lead to other manifestations of the J-wave syndrome.

Na(v)1.5, the cardiac isoform of the voltage-gated Na+ channel, is critical to heart excitability and conduction. However, the mechanisms regulating its expression at the cell membrane are poorly understood. The Na(v)1.5 C-terminus contains a PY-motif (xPPxY) that is known to act as binding site for Nedd4/Nedd4-like ubiquitin-protein ligases. Because Nedd4-2 is well expressed in the heart, we investigated its role in the ubiquitination and regulation of Na(v)1.5. Yeast two-hybrid and GST-pulldown experiments revealed an interaction between Na(v)1.5 C-terminus and Nedd4-2, which was abrogated by mutating the essential tyrosine of the PY-motif. Ubiquitination of Na(v)1.5 was detected in both transfected HEK cells and heart extracts. Furthermore, Nedd4-2-dependent ubiquitination of Na(v)1.5 was observed. To test for a functional role of Nedd4-2, patch-clamp experiments were performed on HEK cells expressing wild-type and mutant forms of both Na(v)1.5 and Nedd4-2. Na(v)1.5 current density was decreased by 65% upon Nedd4-2 cotransfection, whereas the PY-motif mutant channels were not affected. In contrast, a catalytically inactive Nedd4-2 had no effect, indicating that ubiquitination mediates this downregulation. However, Nedd4-2 did not alter the whole-cell or the single channel biophysical properties of Na(v)1.5. Consistent with the functional findings, localization at the cell periphery of Na(v)1.5-YFP fusion proteins was reduced upon Nedd4-2 coexpression. The Nedd4-1 isoform did not regulate Na(v)1.5, suggesting that Nedd4-2 is a specific regulator of Na(v)1.5. These results demonstrate that Na(v)1.5 can be ubiquitinated in heart tissues and that the ubiquitin-protein ligase Nedd4-2 acts on Na(v)1.5 by decreasing the channel density at the cell surface.