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Browsing QOB - Artigos by Subject "Animals"
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- PLK4 trans-Autoactivation Controls Centriole Biogenesis in SpacePublication . Lopes, Carla A.M.; Jana, Swadhin Chandra; Cunha-Ferreira, Inês; Zitouni, Sihem; Bento, Inês; Duarte, Paulo; Gilberto, Samuel; Freixo, Francisco; Guerrero, Adán; Francia, Maria; Lince-Faria, Mariana; Carneiro, Jorge; Bettencourt-Dias, MónicaCentrioles are essential for cilia and centrosome assembly. In centriole-containing cells, centrioles always form juxtaposed to pre-existing ones, motivating a century-old debate on centriole biogenesis control. Here, we show that trans-autoactivation of Polo-like kinase 4 (PLK4), the trigger of centriole biogenesis, is a critical event in the spatial control of that process. We demonstrate that centrioles promote PLK4 activation through its recruitment and local accumulation. Though centriole removal reduces the proportion of active PLK4, this is rescued by concentrating PLK4 to the peroxisome lumen. Moreover, while mild overexpression of PLK4 only triggers centriole amplification at the existing centriole, higher PLK4 levels trigger both centriolar and cytoplasmatic (de novo) biogenesis. Hence, centrioles promote their assembly locally and disfavor de novo synthesis. Similar mechanisms enforcing the local concentration and/or activity of other centriole components are likely to contribute to the spatial control of centriole biogenesis under physiological conditions.
- 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.