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Register a new userA new dominance concept and its application to diversity-stability analysis
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We introduce a new dominance concept consisting of three new dominance metrics based on Lloyds (1967) mean crowding index. The new metrics link communities and species, whereas existing ones are applicable only to communities. Our community-level metric is a function of Simpsons diversity index. For species, our metric quantifies the difference between community dominance and the dominance of a virtual community whose mean population size (per species) equals the population size of the focal species. The new metrics have at least two immediate applications: (i) acting as proxies for diversity in diversity-stability modeling (ii) replacing population abundance in reconstructing species dominance networks. The first application is demonstrated here using data from a longitudinal study of the human vaginal microbiome, and provides new insights relevant for microbial community stability and disease etiology.
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A multilayer network approach combines different network layers, which are connected by interlayer edges, to create a single mathematical object. These networks can contain a variety of information types and represent different aspects of a system. However, the process for selecting which information to include is not always straightforward. Using data on two agonistic behaviors in a captive population of monk parakeets (Myiopsitta monachus), we developed a framework for investigating how pooling or splitting behaviors at the scale of dyadic relationships (between two individuals) affects individual- and group-level social properties. We designed two reference models to test whether randomizing the number of interactions across behavior types results in similar structural patterns as the observed data. Although the behaviors were correlated, the first reference model suggests that the two behaviors convey different information about some social properties and should therefore not be pooled. However, once we controlled for data sparsity, we found that the observed measures corresponded with those from the second reference model. Hence, our initial result may have been due to the unequal frequencies of each behavior. Overall, our findings support pooling the two behaviors. Awareness of how selected measurements can be affected by data properties is warranted, but nonetheless our framework disentangles these efforts and as a result can be used for myriad types of behaviors and questions. This framework will help researchers make informed and data-driven decisions about which behaviors to pool or separate, prior to using the data in subsequent multilayer network analyses.
Predictive high-fidelity finite element simulations of human cardiac mechanics co-mmon-ly require a large number of structural degrees of freedom. Additionally, these models are often coupled with lumped-parameter models of hemodynamics. High computational demands, however, slow down model calibration and therefore limit the use of cardiac simulations in clinical practice. As cardiac models rely on several patient-specific parameters, just one solution corresponding to one specific parameter set does not at all meet clinical demands. Moreover, while solving the nonlinear problem, 90% of the computation time is spent solving linear systems of equations. We propose a novel approach to reduce only the structural dimension of the monolithically coupled structure-windkessel system by projection onto a lower-dimensional subspace. We obtain a good approximation of the displacement field as well as of key scalar cardiac outputs even with very few reduced degrees of freedom while achieving considerable speedups. For subspace generation, we use proper orthogonal decomposition of displacement snapshots. To incorporate changes in the parameter set into our reduced order model, we provide a comparison of subspace interpolation methods. We further show how projection-based model order reduction can be easily integrated into a gradient-based optimization and demonstrate its performance in a real-world multivariate inverse analysis scenario. Using the presented projection-based model order reduction approach can significantly speed up model personalization and could be used for many-query tasks in a clinical setting.
Metapopulations are models of ecological systems, describing the interactions and the behavior of populations that live in fragmented habitats. In this paper, we present a model of metapopulations based on the multivolume simulation algorithm tau-DPP, a stochastic class of membrane systems, that we utilize to investigate the influence that different habitat topologies can have on the local and global dynamics of metapopulations. In particular, we focus our analysis on the migration rate of individuals among adjacent patches, and on their capability of colonizing the empty patches in the habitat. We compare the simulation results obtained for each habitat topology, and conclude the paper with some proposals for other research issues concerning metapopulations.
RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced transcript isoforms of a gene. A central question is to understand the regulatory processes that lead to differences in relative abundance variation due to external and genetic factors. Here, we present a mixed model approach that allows for (i) joint analysis and genetic mapping of multiple transcript isoforms and (ii) mapping of isoform-specific effects. Central to our approach is to comprehensively model the causes of variation and correlation between transcript isoforms, including the genomic background and technical quantification uncertainty. As a result, our method allows to accurately test for shared as well as transcript-specific genetic regulation of transcript isoforms and achieves substantially improved calibration of these statistical tests. Experiments on genotype and RNA-Seq data from 126 human HapMap individuals demonstrate that our model can help to obtain a more fine-grained picture of the genetic basis of gene expression variation.
We perform the linear stability analysis for a new model for poromechanical processes with inertia (formulated in mixed form using the solid deformation, fluid pressure, and total pressure) interacting with diffusing and reacting solutes convected in the medium. We find parameter regions that lead to spatio-temporal instabilities of the coupled system. The mutual dependences between deformation and diffusive patterns are of substantial relevance in the study of morphoelastic changes in biomaterials. We provide a set of computational examples in 2D and 3D (related to brain mechanobiology) that can be used to form a better understanding on how, and up to which extent, the deformations of the porous structure dictate the generation and suppression of spatial patterning dynamics, also related to the onset of mechano-chemical waves.
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