Browsing by Issue Date, starting with "2015-02-02"
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- Dissection of miRNA Pathways Using Arabidopsis Mesophyll ProtoplastsPublication . Martinho, Cláudia; Confraria, Ana; Elias, Carlos Alexandre; Crozet, Pierre; Rubio-Somoza, Ignacio; Weigel, Detlef; Baena-González, ElenaMicroRNAs (miRNAs) control gene expression mostly post-transcriptionally by guiding transcript cleavage and/or translational repression of complementary mRNA targets, thereby regulating developmental processes and stress responses. Despite the remarkable expansion of the field, the mechanisms underlying miRNA activity are not fully understood. In this article, we describe a transient expression system in Arabidopsis mesophyll protoplasts, which is highly amenable for the dissection of miRNA pathways. We show that by transiently overexpressing primary miRNAs and target mimics, we can manipulate miRNA levels and consequently impact on their targets. Furthermore, we developed a set of luciferase-based sensors for quantifying miRNA activity that respond specifically to both endogenous and overexpressed miRNAs and target mimics. We demonstrate that these miRNA sensors can be used to test the impact of putative components of the miRNA pathway on miRNA activity, as well as the impact of specific mutations, by either overexpression or the use of protoplasts from the corresponding mutants. We further show that our miRNA sensors can be used for investigating the effect of chemicals on miRNA activity. Our cell-based transient expression system is fast and easy to set up, and generates quantitative results, being a powerful tool for assaying miRNA activity in vivo.
- To err is robotic, to tolerate immunological: fault detection in multirobot systemsPublication . Tarapore, Danesh; Lima, Pedro U; Carneiro, Jorge; Christensen, Anders LyhneFault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space.