912 resultados para Crete, Greece
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Post-transcriptional cleavage of RNA molecules to generate smaller fragments is a widespread mechanism that enlarges the structural and functional complexity of cellular RNomes. In particular, fragments deriving from both precursor and mature tRNAs represent one of the rapidly growing classes of post-transcriptional RNA pieces. Importantly, these tRNA-derived fragments (tRFs) possess distinct expression patterns, abundance, cellular localizations, or biological roles compared with their parental tRNA molecules [1]. Here we present evidence that tRFs from the archaeon Haloferax volcanii directly bind to ribosomes. In a previous genomic screen for ribosome-associated small RNAs we have identified a 26 residue long fragment originating from the 5’ part of valine tRNA (Val-tRF) to be by far the most abundant tRF in H. volcanii [2]. The Val-tRF is processed in a stress- dependent manner and was found to primarily target the small ribosomal subunit in vitro and in vivo. Translational activity was markedly reduced in the presence of Val-tRF, while control RNA fragments of similar length did not show inhibition of protein biosynthesis. Crosslinking experiments and subsequent primer extension analyses revealed the Val-tRF interaction site to surround the mRNA path in the 30S subunit. In support of this, binding experiments demonstrated that Val-tRF does compete with mRNAs for ribosome binding. Therefore this tRF represents a ribosome-associated non-protein-coding RNA (rancRNA) capable of regulating gene expression in H. volcanii under environmental stress conditions probably by fine-tuning the rate of protein production [3].
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In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.
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Use of computational fluid dynamic (CFD) methods to predict the power production from wind entire wind farms in flat and complex terrain is presented in this paper. Two full 3D Navier–Stokes solvers for incompressible flow are employed that incorporate the k–ε and k–ω turbulence models respectively. The wind turbines (W/Ts) are modelled as momentum absorbers by means of their thrust coefficient using the actuator disk approach. The WT thrust is estimated using the wind speed one diameter upstream of the rotor at hub height. An alternative method that employs an induction-factor based concept is also tested. This method features the advantage of not utilizing the wind speed at a specific distance from the rotor disk, which is a doubtful approximation when a W/T is located in the wake of another and/or the terrain is complex. To account for the underestimation of the near wake deficit, a correction is introduced to the turbulence model. The turbulence time scale is bounded using the general “realizability” constraint for the turbulent velocities. Application is made on two wind farms, a five-machine one located in flat terrain and another 43-machine one located in complex terrain. In the flat terrain case, the combination of the induction factor method along with the turbulence correction provides satisfactory results. In the complex terrain case, there are some significant discrepancies with the measurements, which are discussed. In this case, the induction factor method does not provide satisfactory results.
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Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked Data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities or providers, and complements existing initiatives such as FIBO. The ap- proach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information.
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Cultural heritage is an important asset of Europe which is largely underexplored. One of the main reasons is that the general public do not really incorporate cultural activities in their life style. Currently, curators and professionals in the heritage sector face the toughest challenges on how to attract, engage and retain visitors of heritage institutions (libraries, museums, archives and historical societies). TAG CLOUD FP7 European project seeks to overcome this situation and promote lifelong engagement with culture by personalising the visitors? cultural experiences through cloud technologies.
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eser-i Hüseyin Kami Hanyavî.
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Includes bibliographical references.
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[EN] The relative number of developing eggs is directly affected by fertilization rate, and unfertile eggs may indirectly negatively affect development of viable eggs within the nest. Thus, the number of viable eggs at laying should influence hatching success. We have studied both parameters in a nesting population of loggerhead turtles from Boavista Island (Republic of Cabo Verde). Fertility was estimated based on eggs excavated from nests within the first 96 hours after deposition. Our results confirm a high egg fertilization rate for the species, which exceeded an average of 94% fertility (95% confidence limits: 91.9 and 96.2%, N=43 nests).
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[EN] Sea turtle nests are exposed to different environmental risks that may affect their hatching success. Human exploitation, predation by wild or domestic animals, nest flooding or severe beach erosion or accession are common causes of egg mortality. However, there is very little information about the impact of microorganisms on turtle eggs. We analyzed loggerhead turtle eggs from Boavista Island (Republic of Cabo Verde) which were incubated under different environmental conditions in order to evaluate the presence and impact of fungus. We have isolated Fusarium oxysporum from dead and live eggs after three days of incubation.