Manchester, NH economic assets profile

Report of key economic assets and strength in Manchester, N.H

Surface radio-mineralisation mediates chelate-free radiolabelling of iron oxide nanoparticles

We introduce the concept of surface radio-mineralisation (SRM) to describe the chelate-free radiolabelling of iron-oxide and ferrite nanoparticles. We demonstrate the effectiveness of SRM with both 111In and 89Zr for bare, polymer-matrix multicore, and surface-functionalised magnetite/maghemite nanoparticles; and for bare Y3Fe5O12 nanoparticles. By analogy with geological mineralisation (the hydrothermal deposition of metals as minerals in ore bodies or lodes) we demonstrate that the heat-induced and aqueous SRM process deposits radiometal-oxides onto the nanoparticle or core surfaces, passing through the matrix or coating if present, without changing the size, structure, or magnetic properties of the nanoparticle or core. We show in a mouse model followed over 7 days that the SRM is sufficient to allow quantitative, non-invasive, prolonged, whole-body localisation of injected nanoparticles with nuclear imaging

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p