Mauricio Barahona

TL;DR: Multiscale community detection is a novel and effective, data-driven method for defining mutually exclusive, collectively exhaustive catchment areas in secondary care in urban areas with dilute healthcare markets.

TL;DR: It is shown how a computationally efficient method for unsupervised graph partitioning can be applied to atomistic graphs derived from protein structures to reveal intrinsic, biochemically relevant substructures at all scales, without re-parameterisation or a priori coarse-graining.

TL;DR: In this paper, a framework for optimal navigation that combines concepts from random walks and optimal control theory is proposed to model how a finite perceptual horizon affects ecological search, and the authors show that, while local strategies are optimal on asymptotically long and short search times, finite perception yields faster convergence and increased search efficiency over transient time scales relevant in biological systems.

TL;DR: In this article, a simple bound on the finite horizon ℒ2/[0, T]-induced norm of a linear time-invariant (LTI) not necessarily stable system which can be efficiently computed by calculating the ℋ∞ norm of the shifted version of the original operator is presented.

TL;DR: In this article, a Gaussian mixture model is proposed to predict the impact of biochemical parameters on metabolite distributions, based on single-cell expression data and standard deterministic models for metabolic pathways.