263 research outputs found

    Trade-off between Time, Space, and Workload: the case of the Self-stabilizing Unison

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    We present a self-stabilizing algorithm for the (asynchronous) unison problem which achieves an efficient trade-off between time, workload, and space in a weak model. Precisely, our algorithm is defined in the atomic-state model and works in anonymous networks in which even local ports are unlabeled. It makes no assumption on the daemon and thus stabilizes under the weakest one: the distributed unfair daemon. In a nn-node network of diameter DD and assuming a period B2D+2B \geq 2D+2, our algorithm only requires O(logB)O(\log B) bits per node to achieve full polynomiality as it stabilizes in at most 2D22D-2 rounds and O(min(n2B,n3))O(\min(n^2B, n^3)) moves. In particular and to the best of our knowledge, it is the first self-stabilizing unison for arbitrary anonymous networks achieving an asymptotically optimal stabilization time in rounds using a bounded memory at each node. Finally, we show that our solution allows to efficiently simulate synchronous self-stabilizing algorithms in an asynchronous environment. This provides a new state-of-the-art algorithm solving both the leader election and the spanning tree construction problem in any identified connected network which, to the best of our knowledge, beat all existing solutions of the literature.Comment: arXiv admin note: substantial text overlap with arXiv:2307.0663

    Making Self-Stabilizing Algorithms for Any Locally Greedy Problem

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    Self-stabilizing algorithms are a way to deal with network dynamicity, as it will update itself after a network change (addition or removal of nodes or edges), as long as changes are not frequent. We propose an automatic transformation of synchronous distributed algorithms that solve locally greedy and mendable problems into self-stabilizing algorithms in anonymous networks. Mendable problems are a generalization of greedy problems where any partial solution may be transformed -instead of completed- into a global solution: every time we extend the partial solution, we are allowed to change the previous partial solution up to a given distance. Locally here means that to extend a solution for a node, we need to look at a constant distance from it. In order to do this, we propose the first explicit self-stabilizing algorithm computing a (k,k-1)-ruling set (i.e. a "maximal independent set at distance k"). By combining this technique multiple times, we compute a distance-K coloring of the graph. With this coloring we can finally simulate Local model algorithms running in a constant number of rounds, using the colors as unique identifiers. Our algorithms work under the Gouda daemon, similar to the probabilistic daemon: if an event should eventually happen, it will occur

    Modulation of the substrate specificity of the kinase PDK1 by distinct conformations of the full-length protein

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    The activation of at least 23 different mammalian kinases requires the phosphorylation of their hydrophobic motifs by the kinase PDK1. A linker connects the phosphoinositide-binding PH domain to the catalytic domain, which contains a docking site for substrates called the PIF pocket. Here, we used a chemical biology approach to show that PDK1 existed in equilibrium between at least three distinct conformations with differing substrate specificities. The inositol polyphosphate derivative HYG8 bound to the PH domain and disrupted PDK1 dimerization by stabilizing a monomeric conformation in which the PH domain associated with the catalytic domain and the PIF pocket was accessible. In the absence of lipids, HYG8 potently inhibited the phosphorylation of Akt (also termed PKB) but did not affect the intrinsic activity of PDK1 or the phosphorylation of SGK, which requires docking to the PIF pocket. In contrast, the small molecule valsartan bound to the PIF pocket and stabilized a second distinct monomeric conformation. Our study reveals dynamic conformations of full-length PDK1 in which the location of the linker and the PH domain relative to the catalytic domain determines the selective phosphorylation of PDK1 substrates. The study further suggests new approaches for the design of drugs to selectively modulate signaling downstream of PDK1

    Multiplication and Modulo are Lattice Linear

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    In this paper, we analyze lattice linearity of multiplication and modulo operations. We demonstrate that these operations are lattice linear and the parallel processing algorithms that we study for both these operations are able to exploit the lattice linearity of their respective problems. This implies that these algorithms can be implemented in asynchronous environments, where the nodes are allowed to read old information from each other and are still guaranteed to converge within the same time complexity. These algorithms also exhibit properties similar to snap-stabilization, i.e., starting from an arbitrary state, the system follows the trace strictly according to its specification

    Making Self-Stabilizing any Locally Greedy Problem

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    We propose a way to transform synchronous distributed algorithms solving locally greedy and mendable problems into self-stabilizing algorithms in anonymous networks. Mendable problems are a generalization of greedy problems where any partial solution may be transformed -- instead of completed -- into a global solution: every time we extend the partial solution we are allowed to change the previous partial solution up to a given distance. Locally here means that to extend a solution for a node, we need to look at a constant distance from it. In order to do this, we propose the first explicit self-stabilizing algorithm computing a (k,k1)(k,k-1)-ruling set (i.e. a "maximal independent set at distance kk"). By combining multiple time this technique, we compute a distance-KK coloring of the graph. With this coloring we can finally simulate \local~model algorithms running in a constant number of rounds, using the colors as unique identifiers. Our algorithms work under the Gouda daemon, which is similar to the probabilistic daemon: if an event should eventually happen, it will occur under this daemon

    Evaluation of the ingestive behaviour of the dairy cow under two systems of rotation with slope

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    The ingestive behaviour of grazing animals is modulated by the vegetation characteristics, topography and the type of stocking method. This research was carried out in 2019, at the Rumipamba CADER-UCE. It aimed to evaluate the impact of two contrasting stocking methods of dairy cows grazing a pasture with an average of slope >8.5%. Four dairy cows were set to graze a 0.4 ha paddock for 5 days for continuous stocking methods, while for the electric fence methods the dairy cows were restricted to 0.2 ha and the fence was moved uphill every 3 hours, repeating this process four times a day. Cow were equipped with activity sensors for 12 h per day. The whole procedure was repeated 2 times after realizing an equalization cuts and both paddocks, a rest time of 30 days and a random reassignment of paddocks to one of the treatments. The cows showed a difference in terms of the percentage of grazing P=0.0072, being higher with the electric fence (55% of the measurement time). From rising-plate-meter estimates of available biomass along the grazing periods, we calculated despite similar forage allowances (electric fence = 48.06 kg DM/cow/d and continuous = 48.21 DM/cow/d) a higher forage intake was obtained in the electric fence treatment (17.5 kg DM/cow/d) compared the continuous stocking (15.7 kg DM/cow/d) (P=0.006). In terms of milk production animals grazing under the differences electrical fence stocking method tended (P=0.0985) to produce more milk (17.39 kg/d) than those grazing in the continuous system (15.16 kg/d) due to the influence of the slope (P=0.05), while for milk quality the protein content was higher for the electric fence (33.7 g/l) than the continuous method (30.5 g/l) (P=0.039). None of the other milk properties differed between methods (P>0.05)

    Advances in Reinforcement Learning

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    Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

    Trehalose 6-phosphate promotes seed filling by activating auxin biosynthesis

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    Plants undergo several developmental transitions during their life cycle. One of these, the differentiation of the young embryo from a meristem-like structure into a highly specialized storage organ, is believed to be controlled by local connections between sugars and hormonal response systems. However, we know little about the regulatory networks underpinning the sugar–hormone interactions in developing seeds. By modulating the trehalose 6-phosphate (T6P) content in growing embryos of garden pea (Pisum sativum), we investigate here the role of this signaling sugar during the seed-filling process. Seeds deficient in T6P are compromised in size and starch production, resembling the wrinkled seeds studied by Gregor Mendel. We show also that T6P exerts these effects by stimulating the biosynthesis of the pivotal plant hormone, auxin. We found that T6P promotes the expression of the auxin biosynthesis gene TRYPTOPHAN AMINOTRANSFERASE RELATED2 (TAR2), and the resulting effect on auxin concentrations is required to mediate the T6P-induced activation of storage processes. Our results suggest that auxin acts downstream of T6P to facilitate seed filling, thereby providing a salient example of how a metabolic signal governs the hormonal control of an integral phase transition in a crop plant

    Computational and biochemical characterizations of anhydrobiosis-related intrinsically disordered proteins.

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    Anhydrobiosis is the remarkable phenomenon of “life without water”. It is a common technique found in plant seeds, and a rare technique utilized by some animals to temporarily stop the clock of life and enter a stasis for up to several millennia by removing all of their cellular water. If this phenomenon can be replicated, then biological and medical materials could be stored at ambient temperatures for centuries, which would address research challenges as well as enhance the availability of medicine in areas of the world where refrigeration, freezing, and cold-chain infrastructure are not developed or infeasible. Furthermore, modifying crop tissues could make them resistant to droughts, addressing one of the greatest threats to food stability around the world. This work utilizes a combination of computational techniques and novel approaches to performing biochemistry without water to elucidate the mechanisms of function of specialized proteins that are responsible for anhydrobiosis in animals, particularly the anhydrobiotic cysts of the brine shrimp Artemia franciscana. A detailed evaluation of the chemical properties of anhydrobiosis-related, intrinsically disordered proteins indicates that there are multiple protein-based strategies to achieve anhydrobiosis, but that late embryogenesis abundant (LEA) proteins are the most well understood. However, the mechanisms of LEA protein function have never been demonstrated, resulting in a wide variety of hypotheses regarding their ability to confer desiccation tolerance. This work demonstrates that a group 1 LEA protein, AfLEA1.1, and a group 6 LEA protein, AfrLEA6, undergo liquid-liquid phase separations during desiccation and thereby transiently form novel protective membraneless organelles which partition specific proteins and nucleic acids. These desiccation-induced cellular compartments are a novel mechanism to explain how LEA proteins confer desiccation tolerance, and the drivers of this behavior have been linked to the consensus sequences that define these LEA proteins. Therefore, the separation of aqueous proteins into a specialize compartment during drying is unlikely to only be a function of AfLEA1.1 and AfrLEA6, but actually the mechanism by which group 1 and group 3 LEA proteins function in plant seeds and anhydrobiotic animals. These results indicate that when water is unavailable, anhydrobiotic organisms substitute it with their own solvents

    Anxiety in Relation to Narrative Deficits in Children with Autism Spectrum Disorders

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    Children with Autism are known to present with language delays that affect their ability to relate their thoughts, ideas, feelings and emotions to others. These difficulties in turn reduce their chances in having successful interactions with their peers and may result in elevated anxiety. The main aim of this thesis is to explore the relationship between narrative skills and anxiety in children with Autism. Three studies were conducted, the first sought to confirm the narrative differences between 19 children with ASD and 20 children who are TD on narrative generation production. Results indicate that children with ASD use fewer story grammar elements, have more difficulty with referential accuracy and deviate more form the main story line by adding irrelevant information. Using data from the same groups, the second study evaluated whether the above three narrative measures are correlated to parent-reported anxiety and whether they may be used to predict anxiety. Collectively, having poorer language skills, poorer SG and RA scores, and increased deviation all indicate a child is more likely to experience anxiety. Results also show that AQ, deviation and the interaction variable between AQ and deviation, are significant predictors of anxiety, explaining the variance seen in parent-reported anxiety. The final study involved 3 children with ASD participating in a narrative intervention to determine whether improving narrative abilities would result in an improvement of theory of mind skills and a reduction in anxiety symptoms. The intervention was successful in improving narrative generation performance which coincided with an improvement on social cognitive tasks and a reduction in parent-reported anxiety post intervention. These results support a link between narrative abilities and the presentation of anxiety symptoms in individuals with autism that may be explained by deficits in neurocognitive functioning. The findings also have clinical implications towards improving assessment and treatment protocols by way of language for people with ASD and comorbid anxiety disorders
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