1,653 research outputs found
A mechanistic view of the particulate biodiffusion coefficient: Step lengths, rest periods and transport directions
We link specific mechanisms of biogenous sediment mixing with the commonly used bioturbation coefficient (Db) that describes their bulk effects. Using an isotropic, stationary, unbiased random walk model we mechanistically decompose the particulate bioturbation coefficient into the fundamental dimensions of length and time. The result shows that Db depends directly on the square of the distance particles are moved (step length) and inversely on the elapsed time between movements (rest period). This new decomposition in terms of explicit mechanisms (i.e., animal activities), leads to scaling arguments that large, deposit feeding animals will in nearly all cases dominate biogenous mixing. Paradoxically, such animals often transport particles vertically in an advective fashion (e.g., conveyor-belt feeding), making the widespread fit of the diffusion equation to tracer profiles equivocal. Finite-difference simulations reveal that even in the complete absence of vertical diffusion, rapid diffusive horizontal mixing coupled with vertical advection can produce vertical profiles characteristic of diffusion. We suggest that near-surface horizontal mixing rates by animals far exceed vertical mixing rates in the same stratum and that this anisotropy may persist throughout the surface mixed layer. Thus, despite their apparently good kinematic fit, one-dimensional biodiffusion coefficients may not accurately describe the dynamics of sediment displacement, leading to errors in models of early diagenesis
European wildcat populations are subdivided into five main biogeographic groups: consequences of Pleistocene climate changes or recent anthropogenic fragmentation?
Extant populations of the European wildcat are fragmented across the continent, the likely consequence of recent extirpations due to habitat loss and over-hunting. However, their underlying phylogeographic history has never been reconstructed. For testing the hypothesis that the European wildcat survived the Ice Age fragmented in Mediterranean refuges, we assayed the genetic variation at 31 microsatellites in 668 presumptive European wildcats sampled in 15 European countries. Moreover, to evaluate the extent of subspecies/population divergence and identify eventual wild Ă domestic cat hybrids, we genotyped 26 African wildcats from Sardinia and North Africa and 294 random-bred domestic cats. Results of multivariate analyses and Bayesian clustering confirmed that the European wild and the domestic cats (plus the African wildcats) belong to two well-differentiated clusters (average Ф ST = 0.159, r st = 0.392, P > 0.001; Analysis of molecular variance [AMOVA]). We identified from c. 5% to 10% cryptic hybrids in southern and central European populations. In contrast, wild-living cats in Hungary and Scotland showed deep signatures of genetic admixture and introgression with domestic cats. The European wildcats are subdivided into five main genetic clusters (average Ф ST = 0.103, r st = 0.143, P > 0.001; AMOVA) corresponding to five biogeographic groups, respectively, distributed in the Iberian Peninsula, central Europe, central Germany, Italian Peninsula and the island of Sicily, and in north-eastern Italy and northern Balkan regions (Dinaric Alps). Approximate Bayesian Computation simulations supported late Pleistocene-early Holocene population splittings (from c. 60 k to 10 k years ago), contemporary to the last Ice Age climatic changes. These results provide evidences for wildcat Mediterranean refuges in southwestern Europe, but the evolution history of eastern wildcat populations remains to be clarified. Historical genetic subdivisions suggest conservation strategies aimed at enhancing gene flow through the restoration of ecological corridors within each biogeographic units. Concomitantly, the risk of hybridization with free-ranging domestic cats along corridor edges should be carefully monitored
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
Stress analysis of V-notches with and without cracks, with application to foreign object damage
Published versio
A reaction-diffusion model for the growth of avascular tumor
A nutrient-limited model for avascular cancer growth including cell
proliferation, motility and death is presented. The model qualitatively
reproduces commonly observed morphologies for primary tumors, and the simulated
patterns are characterized by its gyration radius, total number of cancer
cells, and number of cells on tumor periphery. These very distinct
morphological patterns follow Gompertz growth curves, but exhibit different
scaling laws for their surfaces. Also, the simulated tumors incorporate a
spatial structure composed of a central necrotic core, an inner rim of
quiescent cells and a narrow outer shell of proliferating cells in agreement
with biological data. Finally, our results indicate that the competition for
nutrients among normal and cancer cells may be a determinant factor in
generating papillary tumor morphology.Comment: 9 pages, 6 figures, to appear in PR
Evolutionary Games with Affine Fitness Functions: Applications to Cancer
We analyze the dynamics of evolutionary games in which fitness is defined as
an affine function of the expected payoff and a constant contribution. The
resulting inhomogeneous replicator equation has an homogeneous equivalent with
modified payoffs. The affine terms also influence the stochastic dynamics of a
two-strategy Moran model of a finite population. We then apply the affine
fitness function in a model for tumor-normal cell interactions to determine
which are the most successful tumor strategies. In order to analyze the
dynamics of concurrent strategies within a tumor population, we extend the
model to a three-strategy game involving distinct tumor cell types as well as
normal cells. In this model, interaction with normal cells, in combination with
an increased constant fitness, is the most effective way of establishing a
population of tumor cells in normal tissue.Comment: The final publication is available at http://www.springerlink.com,
http://dx.doi.org/10.1007/s13235-011-0029-
Geographic constraints on social network groups
Social groups are fundamental building blocks of human societies. While our
social interactions have always been constrained by geography, it has been
impossible, due to practical difficulties, to evaluate the nature of this
restriction on social group structure. We construct a social network of
individuals whose most frequent geographical locations are also known. We also
classify the individuals into groups according to a community detection
algorithm. We study the variation of geographical span for social groups of
varying sizes, and explore the relationship between topological positions and
geographic positions of their members. We find that small social groups are
geographically very tight, but become much more clumped when the group size
exceeds about 30 members. Also, we find no correlation between the topological
positions and geographic positions of individuals within network communities.
These results suggest that spreading processes face distinct structural and
spatial constraints.Comment: 10 pages, 5 figure
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