1,539 research outputs found

    Circumstellar dust distribution in systems with two planets in resonance

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    We investigate via numerical modeling the effects of two planets locked in resonance, and migrating outward, on the dust distribution of the natal circumstellar disk. We aim to test whether the dust distribution exhibits peculiar features arising from the interplay among the gravitational perturbations of the planets in resonance, the evolution of the gas, and its influence on the dust grains' dynamics. We focus on the 3:2 and 2:1 resonance, where the trapping may be caused by the convergent migration of a Jupiter- and Saturn-mass planet, preceding the common gap formation and ensuing outward (or inward) migration. Models show that a common gap also forms in the dust component -- similarly to what a single, more massive planet would generate -- and that outward migration leads to a progressive widening of the dust gap and to a decoupling from the gas gap. As the system evolves, a significantly wider gap is observed in the dust distribution, which ceases to overlap with the gas gap in the inner disk regions. At the outer edge of the gas gap, outward migration of the planets produces an over-density of dust particles, which evolve differently in the 3:2 and 2:1 resonances. For the 3:2, the dust trap at the gap's outer edge is partly efficient and a significant fraction of the grains filters through the gap. For the 2:1 resonance, the trap is more efficient and very few grains cross the gap, while the vast majority accumulate at the outer edge of the gap.Comment: 16 pages, 12 figures. Accepted for publicatio

    Continuous and Discontinuous Phase Transitions in the evolution of a polygenic trait under stabilizing selective pressure

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    The presence of phenomena analogous to phase transition in Statistical Mechanics, has been suggested in the evolution of a polygenic trait under stabilizing selection, mutation and genetic drift. By using numerical simulations of a model system, we analyze the evolution of a population of NN diploid hermaphrodites in random mating regime. The population evolves under the effect of drift, selective pressure in form of viability on an additive polygenic trait, and mutation. The analysis allows to determine a phase diagram in the plane of mutation rate and strength of selection. The involved pattern of phase transitions is characterized by a line of critical points for weak selective pressure (smaller than a threshold), whereas discontinuous phase transitions, characterized by metastable hysteresis, are observed for strong selective pressure. A finite size scaling analysis suggests the analogy between our system and the mean field Ising model for selective pressure approaching the threshold from weaker values. In this framework, the mutation rate, which allows the system to explore the accessible microscopic states, is the parameter controlling the transition from large heterozygosity (disordered phase) to small heterozygosity (ordered one).Comment: 8 pages, 7 figures, 1 tabl

    Bidimensional Tandem Mass Spectrometry for Selective Identification of Nitration Sites in Proteins.

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    Nitration of protein tyrosine residues is very often regarded as a molecular signal of peroxynitrite formation during development, oxidative stress, and aging. However, protein nitration might also have biological functions comparable to protein phosphorylation, mainly in redox signaling and in signal transduction. The major challenge in the proteomic analysis of nitroproteins is the need to discriminate modified proteins, usually occurring at substoichiometric levels from the large amount of nonmodified proteins. Moreover, precise localization of the nitration site is often required to fully describe the biological process. Existing methodologies essentially rely on immunochemical techniques either using 2D-PAGE fractionation in combination with western blot analyses or exploiting immunoaffinity procedures to selectively capture nitrated proteins. Here we report a totally new approach involving dansyl chloride labeling of the nitration sites that rely on the enormous potential of MSn analysis. The tryptic digest from the entire protein mixture is directly analyzed by MS on a linear ion trap mass spectrometer. Discrimination between nitro- and unmodified peptide is based on two selectivity criteria obtained by combining a precursor ion scan and an MS3 analysis. This new procedure was successfully applied to the identification of 3-nitrotyrosine residues in complex protein mixtures

    Core Muscle Activation in Suspension Training Exercises

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    A quantitative observational laboratory study was conducted to characterize and classify core training exercises executed in a suspension modality on the base of muscle activation. In a prospective single-group repeated measures design, seventeen active male participants performed four suspension exercises typically associated with core training (roll-out, bodysaw, pike and knee-tuck). Surface electromyographic signals were recorded from lower and upper parts of rectus abdominis, external oblique, internal oblique, lower and upper parts of erector spinae muscles using concentric bipolar electrodes. The average rectified values of electromyographic signals were normalized with respect to individual maximum voluntary isometric contraction of each muscle. Roll-out exercise showed the highest activation of rectus abdominis and oblique muscles compared to the other exercises. The rectus abdominis and external oblique reached an activation higher than 60% of the maximal voluntary contraction (or very close to that threshold, 55%) in roll-out and bodysaw exercises. Findings from this study allow the selection of suspension core training exercises on the basis of quantitative information about the activation of muscles of interest. Roll-out and bodysaw exercises can be considered as suitable for strength training of rectus abdominis and external oblique muscles

    Integrating Contextual Knowledge to Visual Features for Fine Art Classification

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    Automatic art analysis has seen an ever-increasing interest from the pattern recognition and computer vision community. However, most of the current work is mainly based solely on digitized artwork images, sometimes supplemented with some metadata and textual comments. A knowledge graph that integrates a rich body of information about artworks, artists, painting schools, etc., in a unified structured framework can provide a valuable resource for more powerful information retrieval and knowledge discovery tools in the artistic domain. To this end, this paper presents ArtGraph: an artistic knowledge graph based on WikiArt and DBpedia. The graph, implemented in Neo4j, already provides knowledge discovery capabilities without having to train a learning system. In addition, the embeddings extracted from the graph are used to inject "contextual" knowledge into a deep learning model to improve the accuracy of artwork attribute prediction tasks

    Integrating Contextual Knowledge to Visual Features for Fine Art Classification

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    Automatic art analysis has seen an ever-increasing interest from the pattern recognition and computer vision community. However, most of the current work is mainly based solely on digitized artwork images, sometimes supplemented with some metadata and textual comments. A knowledge graph that integrates a rich body of information about artworks, artists, painting schools, etc., in a unified structured framework can provide a valuable resource for more powerful information retrieval and knowledge discovery tools in the artistic domain. To this end, this paper presents ArtGraph: an artistic knowledge graph based on WikiArt and DBpedia. The graph, implemented in Neo4j, already provides knowledge discovery capabilities without having to train a learning system. In addition, the embeddings extracted from the graph are used to inject "contextual" knowledge into a deep learning model to improve the accuracy of artwork attribute prediction tasks.Comment: Typos corrected. Added classification experiment. Accepted at DL4KG202

    Considerations about learning Word2Vec

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    AbstractDespite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real capabilities. In particular, to our knowledge, no author seems to have analysed in detail how learning may be affected by the various choices of hyperparameters. In this work, we try to shed some light on various issues focusing on a typical dataset. It is shown that the learning rate prevents the exact mapping of the co-occurrence matrix, that Word2Vec is unable to learn syntactic relationships, and that it does not suffer from the problem of overfitting. Furthermore, through the creation of an ad-hoc network, it is also shown how it is possible to improve Word2Vec directly on the analogies, obtaining very high accuracy without damaging the pre-existing embedding. This analogy-enhanced Word2Vec may be convenient in various NLP scenarios, but it is used here as an optimal starting point to evaluate the limits of Word2Vec

    ArtGraph

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    ArtGraph is a Knowledge Graph in the art domain, based on WikiArt and DBpedia, able to represent and describe concepts related to arworks
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