163 resultados para Competition Map


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There is increasing appreciation that hosts in natural populations are subject to infection by multiple parasite species. Yet the epidemiological and ecological processes determining the outcome of mixed infections are poorly understood. Here, we use two intracellular gut parasites (Microsporidia), one exotic and one co-evolved in the western honeybee (Apis mellifera), in an experiment in which either one or both parasites were administered either simultaneously or sequentially. We provide clear evidence of within-host competition; order of infection was an important determinant of the competitive outcome between parasites, with the first parasite significantly inhibiting the growth of the second, regardless of species. However, the strength of this ‘priority effect’ was highly asymmetric, with the exotic Nosema ceranae exhibiting stronger inhibition of Nosema apis than vice versa. Our results reveal an unusual asymmetry in parasite competition that is dependent on order of infection. When incorporated into a mathematical model of disease prevalence, we find asymmetric competition to be an important predictor of the patterns of parasite prevalence found in nature. Our findings demonstrate the wider significance of complex multi-host–multi-parasite interactions as drivers of host–pathogen community structure

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We study the competing effects of simultaneous Markovian and non-Markovian decoherence mechanisms acting on a single spin. We show the existence of a threshold in the relative strength of such mechanisms above which the spin dynamics becomes fully Markovian, as revealed by the use of several non-Markovianity measures. We identify a measure-dependent nested structure of such thresholds, hinting at a causality relationship among the various non-Markovianity witnesses used in our analysis. Our considerations are then used to argue the unavoidably non-Markovian evolution of a single-electron quantum dot exposed to both intrinsic and Markovian technical noise, the latter of arbitrary strength. 

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Background: Active travel to school can be an important contributor to the total physical activity of children but levels have declined and more novel approaches are required to stimulate this as an habitual behaviour. The aim of this mixed methods study was to investigate the feasibility of an international walk to school competition supported by novel swipecard technology to increase children's walking to/from school. Methods: Children aged 9-13 years old participated in an international walk to school competition to win points for themselves, their school and their country over a 4-week period. Walks to and from school were recorded using swipecard technology and a bespoke website. For each point earned by participants, 1 pence (£0.01) was donated to the charity of the school's choice. The primary outcome was number of walks to/from school objectively recorded using the swipecard tracking system over the intervention period. Other measures included attitudes towards walking collected at baseline and week 4 (post-intervention). A qualitative sub-study involving focus groups with children, parents and teachers provided further insight. Results: A total of 3817 children (mean age 11.5±SD 0.7) from 12 schools in three cities (London and Reading, England and Vancouver, Canada) took part in the intervention, representing a 95% intervention participation rate. Results show a gradual decline in the average number of children walking to and from school over the 4-week period (week 1 mean 29%±SD2.5; week 2 mean 18%±SD3.6; week 3 mean 14%±SD4.0; week 4 mean 12%±SD1.1). Post intervention, 97% of children felt that walking to school helped them stay healthy, feel happy (81%) and stay alert in class (76%). These results are supported by qualitative findings from children, parents and teachers. Key areas for improvement include the need to incorporate strategies for maintenance of behaviour change into the intervention and also to adopt novel methods of data collection to increase follow-up rates. Conclusions: This mixed methods study suggests that an international walk to school competition using innovative technology can be feasibly implemented and offers a novel way of engaging schools and motivating children to walk to school.

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Terrestrial invertebrates constitute most of described animal biodiversity and soil is a major reservoir of this diversity. In the classical attempt to understand the processes supporting biodiversity, ecologists are currently seeking to unravel the differential roles of environmental filtering and competition for resources in niche partitioning processes: these processes are in principle distinct although they may act simultaneously, interact at multiple spatial and temporal scales, and are often confounded in studies of soil communities. We used a novel combination of methods based on stable isotopes and trait analysis to resolve these processes in diverse oribatid mite assemblages at spatial
scales at which competition for resources could in principle be a major driver. We also used a null model approach based on a general neutral model of beta diversity. A large and significant fraction of community variation was explainable in terms of linear and periodic spatial structures in the distribution of organic C, N and soil structure: species were clearly arranged along an environmental, spatially structured gradient. However, competition related trait differences did not map onto the distances separating species along the environmental gradient and neutral models provided a satisfying approximation of beta diversity patterns. The results represent the first robust evidence
that in very diverse soil arthropod assemblages resource-based niche partitioning plays a minor role while environmental filtering remains a fundamental driver of species distribution.

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This work examines the conformational ensemble involved in β-hairpin folding by means of advanced molecular dynamics simulations and dimensionality reduction. A fully atomistic description of the protein and the surrounding solvent molecules is used, and this complex energy landscape is sampled by means of parallel tempering metadynamics simulations. The ensemble of configurations explored is analyzed using the recently proposed sketch-map algorithm. Further simulations allow us to probe how mutations affect the structures adopted by this protein. We find that many of the configurations adopted by a mutant are the same as those adopted by the wild-type protein. Furthermore, certain mutations destabilize secondary-structure-containing configurations by preventing the formation of hydrogen bonds or by promoting the formation of new intramolecular contacts. Our analysis demonstrates that machine-learning techniques can be used to study the energy landscapes of complex molecules and that the visualizations that are generated in this way provide a natural basis for examining how the stabilities of particular configurations of the molecule are affected by factors such as temperature or structural mutations.

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We study the sensitivity of a MAP configuration of a discrete probabilistic graphical model with respect to perturbations of its parameters. These perturbations are global, in the sense that simultaneous perturbations of all the parameters (or any chosen subset of them) are allowed. Our main contribution is an exact algorithm that can check whether the MAP configuration is robust with respect to given perturbations. Its complexity is essentially the same as that of obtaining the MAP configuration itself, so it can be promptly used with minimal effort. We use our algorithm to identify the largest global perturbation that does not induce a change in the MAP configuration, and we successfully apply this robustness measure in two practical scenarios: the prediction of facial action units with posed images and the classification of multiple real public data sets. A strong correlation between the proposed robustness measure and accuracy is verified in both scenarios.

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This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian networks, which is the problem of querying the most probable state configuration of some of the network variables given evidence. It is demonstrated that the problem remains hard even in networks with very simple topology, such as binary polytrees and simple trees (including the Naive Bayes structure), which extends previous complexity results. Furthermore, a Fully Polynomial Time Approximation Scheme for MAP in networks with bounded treewidth and bounded number of states per variable is developed. Approximation schemes were thought to be impossible, but here it is shown otherwise under the assumptions just mentioned, which are adopted in most applications.

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This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian networks, which is the problem of querying the most probable state configuration of some of the network variables given evidence. First, it is demonstrated that the problem remains hard even in networks with very simple topology, such as binary polytrees and simple trees (including the Naive Bayes structure). Such proofs extend previous complexity results for the problem. Inapproximability results are also derived in the case of trees if the number of states per variable is not bounded. Although the problem is shown to be hard and inapproximable even in very simple scenarios, a new exact algorithm is described that is empirically fast in networks of bounded treewidth and bounded number of states per variable. The same algorithm is used as basis of a Fully Polynomial Time Approximation Scheme for MAP under such assumptions. Approximation schemes were generally thought to be impossible for this problem, but we show otherwise for classes of networks that are important in practice. The algorithms are extensively tested using some well-known networks as well as random generated cases to show their effectiveness.

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This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem in Bayesian networks with topology of trees (every variable has at most one parent) and variable cardinality at most three. MAP is the problem of querying the most probable state configuration of some (not necessarily all) of the network variables given evidence. It is demonstrated that the problem remains hard even in such simplistic networks.

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This paper presents a new anytime algorithm for the marginal MAP problem in graphical models of bounded treewidth. We show asymptotic convergence and theoretical error bounds for any fixed step. Experiments show that it compares well to a state-of-the-art systematic search algorithm.

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We consider a multi-market framework where a set of firms compete on two oligopolistic markets. The cost of production of each firm allows for spillovers across markets, ensuring that output decisions for both markets have to be made jointly. Prior to competing in these markets, firms can establish links gathering business intelligence about other firms. A link formed by a firm generates two types of externalities for competitors and consumers. We characterize the business intelligence equilibrium networks and networks that maximize social welfare. By contrast with single market competition, we show that in multi-market competition there exist situations where intelligence gathering activities are underdeveloped with regard to social welfare and should be tolerated, if not encouraged, by public authorities.

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The X-linked lymphoproliferative syndrome (XLP) is an inherited immuno-deficiency to Epstein-Barr virus infection that has been mapped to chromosome Xq25. Molecular analysis of XLP patients from ten different families identified a small interstitial constitutional deletion in 1 patient (XLP-D). This deletion, initially defined by a single marker, DF83, known to map to interval Xq24-q26.1, is nested within a previously reported and much larger deletion in another XLP patient (XLP-739). A cosmid minilibrary was constructed from a single mega-YAC and used to establish a contig encompassing the whole XLP-D deletion and a portion of the XLP-739 deletion. Based on this contig, the size of the XLP-D deletion can be estimated at 130 kb. The identification of this minimal deletion, within which at least a portion of the XLP gene is likely to reside, should greatly facilitate efforts in isolating the gene.