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BackgroundIdentification of essential genes is not only useful for our understanding of the minimal gene set required for cellular life but also aids the identification of novel drug targets in pathogens. In this work, we present a simple and effective gene essentiality prediction method using information-theoretic features that are derived exclusively from the gene sequences.ResultsWe developed a Random Forest classifier and performed an extensive model performance evaluation among and within 15 selected bacteria. In intra-organism predictions, where training and testing sets are taken from the same organism, AUC (Area Under the Curve) scores ranging from 0.73 to 0.90, 0.84 on average, were obtained. Cross-organism predictions using 5-fold cross-validation, pairwise, leave-one-species-out, leave-one-taxon-out, and cross-taxon yielded average AUC scores of 0.88, 0.75, 0.80, 0.82, and 0.78, respectively. To further show the applicability of our method in other domains of life, we predicted the essential genes of the yeast Schizosaccharomyces pombe and obtained a similar accuracy (AUC 0.84).ConclusionsThe proposed method enables a simple and reliable identification of essential genes without searching in databases for orthologs and demanding further experimental data such as network topology and gene-expression.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1884-5) contains supplementary material, which is available to authorized users.

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This paper proposes a suitable method for simulating impulses with appropriate amplitude, spectral, and inter-arrival characteristics. The statistics used to develop the parameters of this model are based on statistics derived from observations of impulse noise on the telephone networks of British Telecom (BT) and Deutsche Telekom (DT). This paper initially reviews the former DT approach to impulse noise generation for testing digital subscriber line systems, so called xDSL systems. Some problems are highlighted and an alternative technique is suggested that is capable of generating impulses with both appropriate amplitude and spectral characteristics.

Understanding emotions is necessary to analyse underlying motivations, values and drivers for behaviours. In landscapes that are rapidly changing, for example, due to land conversion for intensive agriculture, a sense of powerlessness of the inhabitants can be common, which may negatively influence their emotional bond to the landscape they are living in. To uncover varied emotional responses towards landscape change we used an innovative approach that combined transdisciplinary and artistic research in an intensively farmed landscape in Germany. In this project, we focused on the topic of favourite places in public spaces, and how change in such places was experienced. Drawing on workshops and interviews, we identified themes of externally driven societal and internal personal influences on the public favourite places. “Resilient” emotional responses towards landscape change showed a will to integrate the modifications, while “non-resilient” responses were characterised by frustration and despair. We argue that identifying emotions towards change can be valuable to strengthen adaptive capacity and to foster sustainability.