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By 1990, publications on Oromo by Western scholars and by Oromo themselves have increased, with the focus ranging from geographical and theoretical aspects of that selfperceived Oromo nation and the wider processes of historical disruption in the lands pertinent to the Oromo in Ethiopia. The use of a common alphabet and a common spelling for the Oromo language has been a consistent point of divergence. Another area of constant challenge was the issue of identity rather than ethnicity.

Little is known about the molecular basis of the influence of external carbon/nitrogen (C/N) ratio and other abiotic factors on phytohormones regulation during seed germination and plant developmental processes, and the identification of elements that participate in this response is essential to understand plant nutrient perception and signaling. Sugars (sucrose, glucose) and nitrate not only act as nutrients but also as signaling molecules in plant development. A connection between changes in auxin transport and nitrate signal transduction has been reported in Arabidopsis thaliana through the NRT1.1, a nitrate sensor and transporter that also functions as a repressor of lateral root growth under low concentrations of nitrate by promoting auxin transport. Nitrate inhibits the elongation of lateral roots, but this effect is significantly reduced in abscisic acid (ABA)-insensitive mutants, what suggests that ABA might mediate the inhibition of lateral root elongation by nitrate. Gibberellin (GA) biosynthesis has been also related to nitrate level in seed germination and its requirement is determined by embryonic ABA. These mechanisms connect nutrients and hormones signaling during seed germination and plant development. Thus, the genetic identification of the molecular components involved in nutrients-dependent pathways would help to elucidate the potential crosstalk between nutrients, nitric oxide (NO) and phytohormones (ABA, auxins and GAs) in seed germination and plant development. In this review we focus on changes in C and N levels and how they control seed germination and plant developmental processes through the interaction with other plant growth regulators, such as phytohormones. ; This research was supported by grant AGL2012-33268 from the Spanish Ministerio de Economía y Competitividad from the FP7 and The European Regional Development Fund (FEDER) from the European Union. ; Peer reviewed ; Peer Reviewed

Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a set of LiDAR data (0.87 points/m 2 ) provided by the Spanish Government (PNOA Programme) over a complex working area mainly covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.