2,241 research outputs found
Without magic bullets: the biological basis for public health interventions against protein folding disorders
Protein folding disorders of aging like Alzheimer's and Parkinson's diseases currently present intractable medical challenges. 'Small molecule' interventions - drug treatments - often have, at best, palliative impact, failing to alter disease course. The design of individual or population level interventions will likely require a deeper understanding of protein folding and its regulation than currently provided by contemporary 'physics' or culture-bound medical magic bullet models. Here, a topological rate distortion analysis is applied to the problem of protein folding and regulation that is similar in spirit to Tlusty's (2010a) elegant exploration of the genetic code. The formalism produces large-scale, quasi-equilibrium 'resilience' states representing normal and pathological protein folding regulation under a cellular-level cognitive paradigm similar to that proposed by Atlan and Cohen (1998) for the immune system. Generalization to long times produces diffusion models of protein folding disorders in which epigenetic or life history factors determine the rate of onset of regulatory failure, in essence, a premature aging driven by familiar synergisms between disjunctions of resource allocation and need in the context of socially or physiologically toxic exposures and chronic powerlessness at individual and group scales. Application of an HPA axis model is made to recent observed differences in Alzheimer's onset rates in White and African American subpopulations as a function of an index of distress-proneness
Snout Shape in Extant Ruminants
Copyright: © 2014 Tennant, MacLeod. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. [4.0 license]. The attached file is the published version of the article
Embedding Population Dynamics Models in Inference
Increasing pressures on the environment are generating an ever-increasing
need to manage animal and plant populations sustainably, and to protect and
rebuild endangered populations. Effective management requires reliable
mathematical models, so that the effects of management action can be predicted,
and the uncertainty in these predictions quantified. These models must be able
to predict the response of populations to anthropogenic change, while handling
the major sources of uncertainty. We describe a simple ``building block''
approach to formulating discrete-time models. We show how to estimate the
parameters of such models from time series of data, and how to quantify
uncertainty in those estimates and in numbers of individuals of different types
in populations, using computer-intensive Bayesian methods. We also discuss
advantages and pitfalls of the approach, and give an example using the British
grey seal population.Comment: Published at http://dx.doi.org/10.1214/088342306000000673 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
- âŠ