Ribosome-associated ncRNAs (rancRNAs) An emerging class of translation regulators

Small non-protein-coding RNA (ncRNA) molecules represent major contributors to regulatory networks in controlling gene expression in a highly efficient manner. Most of the recently discovered regulatory ncRNAs acting on translation target the mRNA rather than the ribosome (e.g.: miRNAs, siRNAs, antisense RNAs). To address the question, whether ncRNA regulators exist that are capable of modulating the rate of protein production by directly interacting with the ribosome, we have analyzed the small ncRNA interactomes of ribosomes. Deep-sequencing analyses revealed thousands of putative rancRNAs in various model organisms (1,2). For a subset of these ncRNA candidates we have gathered experimental evidence that they associate with ribosomes in a stress-dependent manner and fine-tune the rate of protein biosynthesis (3,4). Many of the investigated rancRNAs appear to be processing products of larger functional RNAs, such as tRNAs (2,3), mRNAs (3), or snoRNAs (2). Post-transcriptional cleavage of RNA to generate smaller fragments is a widespread mechanism that enlarges the structural and functional complexity of cellular RNomes. Our data disclose the ribosome as target for small regulatory RNAs. rancRNAs are found in all domains of life and represent a prevalent but so far largely unexplored class of regulatory molecules (5). Ongoing work in our lab revealed first insight into rancRNA processing and mechanism of this emerging class of translation regulators.

An automated method for mapping physical soil and water conservation structures on cultivated land using GIS and remote sensing techniques

An efficient and reliable automated model that can map physical Soil and Water Conservation (SWC) structures on cultivated land was developed using very high spatial resolution imagery obtained from Google Earth and ArcGIS, ERDAS IMAGINE, and SDC Morphology Toolbox for MATLAB and statistical techniques. The model was developed using the following procedures: (1) a high-pass spatial filter algorithm was applied to detect linear features, (2) morphological processing was used to remove unwanted linear features, (3) the raster format was vectorized, (4) the vectorized linear features were split per hectare (ha) and each line was then classified according to its compass direction, and (5) the sum of all vector lengths per class of direction per ha was calculated. Finally, the direction class with the greatest length was selected from each ha to predict the physical SWC structures. The model was calibrated and validated on the Ethiopian Highlands. The model correctly mapped 80% of the existing structures. The developed model was then tested at different sites with different topography. The results show that the developed model is feasible for automated mapping of physical SWC structures. Therefore, the model is useful for predicting and mapping physical SWC structures areas across diverse areas.