Dynamics of carbon pools in post-agrogenic sandy soils of southern taiga of Russia

<p>Abstract</p> <p>Background</p> <p>Until recently, a lot of arable lands were abandoned in many countries of the world and, especially, in Russia, where about half a million square kilometers of arable lands were abandoned in 1961-2007. The soils at these fallows undergo a process of natural restoration (or self-restoration) that changes the balance of soil organic matter (SOM) supply and mineralization.</p> <p>Results</p> <p>A soil chronosequence study, covering the ecosystems of 3, 20, 55, 100, and 170 years of self-restoration in southern taiga zone, shows that soil organic content of mineral horizons remains relatively stable during the self-restoration. This does not imply, however, that SOM pools remain steady. The C/N ratio of active SOM reached steady state after 55 years, and increased doubly (from 12.5 - 15.6 to 32.2-33.8). As to the C/N ratio of passive SOM, it has been continuously increasing (from 11.8-12.7 to 19.0-22.8) over the 170 years, and did not reach a steady condition.</p> <p>Conclusion</p> <p>The results of the study imply that soil recovery at the abandoned arable sandy lands of taiga is incredibly slow process. Not only soil morphological features of a former ploughing remained detectable but also the balance of soil organic matter input and mineralization remained unsteady after 170 years of self-restoration.</p

Ligand-target prediction by structural network biology using nAnnoLyze

Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for "anonymous" compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound-protein interactions and thus may benefit drug development