Does the brain listen to the gut?

Transplanting gut bacteria from one mouse strain to another can override genetics and change behavior

Approximations of countably-infinite linear programs over bounded measure spaces

We study a class of countably-infinite-dimensional linear programs (CILPs) whose feasible sets are bounded subsets of appropriately defined weighted spaces of measures. We show how to approximate the optimal value, optimal points, and minimal points of these CILPs by solving finite-dimensional linear programs. The errors of our approximations converge to zero as the size of the finite-dimensional program approaches that of the original problem and are easy to bound in practice. We discuss the use of our methods in the computation of the stationary distributions, occupation measures, and exit distributions of Markov~chains

Introducing the microbiome into Precision Medicine

© The Author(s), 2016. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Trends in Pharmacological Sciences 38 (2017): 81-91, doi:10.1016/j.tips.2016.10.001.Understanding how individual people respond to medical therapy is a key facet of improving the odds ratio that interventions will have a positive impact. Reducing the non-responder rate for an intervention or reducing complications associated with a particular treatment or surgery is the next stage of medical advance. The Precision Medicine Initiative, launched in January 2015, set the stage for enhanced collaboration between researchers and medical professionals to develop next-generation techniques to aid patient treatment and recovery, and increased the opportunities for impactful pre-emptive care. The microbiome plays a crucial role in health and disease, as it influences endocrinology, physiology, and even neurology, altering the outcome of many different disease states, and it augments drug responses and tolerance. We review the implications of the microbiome on precision health initiatives and highlight excellent examples, whereby precision microbiome health has been implemented.2017-11-0

OMCat: Catalogue of Serendipitous Sources Detected with the XMM-Newton Optical Monitor

The Optical Monitor Catalogue of serendipitous sources (OMCat) contains entries for every source detected in the publicly available XMM-Newton Optical Monitor (OM) images taken in either the imaging or ``fast'' modes. Since the OM is coaligned and records data simultaneously with the X-ray telescopes on XMM-Newton, it typically produces images in one or more near-UV/optical bands for every pointing of the observatory. As of the beginning of 2006, the public archive had covered roughly 0.5% of the sky in 2950 fields. The OMCat is not dominated by sources previously undetected at other wavelengths; the bulk of objects have optical counterparts. However, the OMCat can be used to extend optical or X-ray spectral energy distributions for known objects into the ultraviolet, to study at higher angular resolution objects detected with GALEX, or to find high-Galactic-latitude objects of interest for UV spectroscopy.Comment: 25 pages, 22 figures, submitted to PAS

ERP: a computerized geo-information data bank for environmental resource planning

Call number: LD2668 .P7 1975 K8

Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations

Computing the stationary distributions of a continuous-time Markov chain involves solving a set of linear equations. In most cases of interest, the number of equations is infinite or too large, and cannot be solved analytically or numerically. Several approximation schemes overcome this issue by truncating the state space to a manageable size. In this review, we first give a comprehensive theoretical account of the stationary distributions and their relation to the long-term behaviour of the Markov chain, which is readily accessible to non-experts and free of irreducibility assumptions made in standard texts. We then review truncation-based approximation schemes paying particular attention to their convergence and to the errors they introduce, and we illustrate their performance with an example of a stochastic reaction network of relevance in biology and chemistry. We conclude by elaborating on computational trade-offs associated with error control and some open questions