In situ observations of fish associated with coral reefs off Ireland

The abundance and behaviour of fish on and around coral reefs at Twin Mounds and Giant Mounds, carbonate mounds located on the continental shelf off Ireland (600-1100. m), were studied using two Remotely Operated Vehicle (ROV) dives. We recorded 30 fish taxa on the dives, together with three species of Scleractinia (Lophelia pertusa, Madrepora oculata and Desmophyllum cristagalli) and a diverse range of other corals (Antipatharia, Alcyonacea, and Stylasteridae). Stands of live coral provided the only habitat in which Guttigadus latifrons was observed whereas Neocyttus helgae was found predominantly on structural habitats provided by dead coral. Significantly more fish were found on structurally complex coral rubble habitats than on flatter areas where coral rubble was clogged with sand. The most common species recorded was Lepidion eques (2136 individuals), which always occurred a few cm above bottom and was significantly more active on the reefs than on sedimentary habitats. Synaphobranchus kaupii (1157 indiv.). , N. helgae (198 indiv.) and Micromesistius poutassou (116 indiv.) were also common; S. kaupii did not exhibit habitat-related differences in behaviour, whilst N. helgae was more active over the reefs and other structured habitats whereas M. poutassou was more active with decreasing habitat complexity. Trawl damage and abandoned fishing gear was observed at both sites. We conclude that Irish coral reefs provide complex habitats that are home to a diverse assemblage of fish utilising the range of niches occurring both above and within the reef structure. © 2011 Elsevier Ltd

Benefits realisation for healthcare

Following the emergent importance of benefits realisation applied to healthcare infrastructure and service development programs, HaCIRIC has undertaken a research initiative targeting the development of a robust and comprehensive Benefits Realisation (BeReal©) process. The resulting model is focusing on how benefits should be elicited at the initial strategic stages, and how benefits should be deployed, managed and traced along the lifecycle of a programme so their realisation contributes to successful health outcomes. Subsequently BeReal© aspires to be an appropriate method to drive and control the programme plan; providing tools and techniques for defining specific benefits. It also allows the measurement and evaluation of the extent to which those benefits are delivered. We have set ourselves the objective of identifying current best practices and demonstrate how to improve benefits realisation in healthcare infrastructure provision. The HaCIRIC team in active collaboration with leading industry partners have undertaken various case and comparator studies not only to define a business critical process but to set out an ideology which places benefits realisation at the heart of securing wholly integrated (collective) change. We believe that to deliver consistent high quality infrastructure and services within an ever changing investment model requires a different level of thinking and understanding towards benefits realisation. The challenge of answering community needs through intelligent investment in infrastructure is complex and demands a deeper and inclusive awareness and appreciation of how to deliver benefits and effectively allocate resources. The BeReal© initiative seeks to contribute methodologically and intends to help spending money intelligently, working with programme and project related stakeholders, securing that the best possible benefits are obtained for the overall healthcare communities. This report highlights selected performed initiatives and summarises BeReal© process’s major characteristics, covering far more than the follow-up of a competitive tendering process and of the development of a traditional business case. BeReal© copes with a detailed definition of changing activities, breakdown of (needs into) benefits that drive the investment, supports decision-making, proposes the development of controlling initiatives and suggests major awareness to the implementation of corrective actions. We seek to continue innovating, stimulate learning, contributing to an increase of health and care performance that properly answers to community needs and intelligently invests public and private resources

Identifying structural changes with unsupervised machine learning methods

Unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, k-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems

Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios

Nitric Oxide Bioavailability and Its Potential Relevance to the Variation in Susceptibility to the Renal and Vascular Complications in Patients With Type 2 Diabetes

OBJECTIVE—We compared the renal and systemic vascular (renovascular) response to a reduction of bioavailable nitric oxide (NO) in type 2 diabetic patients without nephropathy and of African and Caucasian heritage. RESEARCH DESIGN AND METHODS—Under euglycemic conditions, renal blood flow was determined by a constant infusion of paraminohippurate and changes in blood pressure and renal vascular resistance estimated before and after an infusion of l-Ng-monomethyl-l-arginine. RESULTS—In the African-heritage group, there was a significant fall in renal blood flow (Δ−46.0 ml/min per 1.73 m(2); P < 0.05) and rise in systolic blood pressure (Δ10.0 mmHg [95% CI 2.3–17.9]; P = 0.017), which correlated with an increase in renal vascular resistance (r(2) = 0.77; P = 0.004). CONCLUSIONS—The renal vasoconstrictive response associated with NO synthase inhibition in this study may be of relevance to the observed vulnerability to renal injury in patients of African heritage