5 resultados para predictive habitat mapping

em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast


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Sediment particle size analysis (PSA) is routinely used to support benthic macrofaunal community distribution data in habitat mapping and Ecological Status (ES) assessment. No optimal PSA Method to explain variability in multivariate macrofaunal distribution has been identified nor have the effects of changing sampling strategy been examined. Here, we use benthic macrofaunal and PSA grabs from two embayments in the south of Ireland. Four frequently used PSA Methods and two common sampling strategies are applied. A combination of laser particle sizing and wet/dry sieving without peroxide pre-treatment to remove organics was identified as the optimal Method for explaining macrofaunal distributions. ES classifications and EUNIS sediment classification were robust to changes in PSA Method. Fauna and PSA samples returned from the same grab sample significantly decreased macrofaunal variance explained by PSA and caused ES to be classified as lower. Employing the optimal PSA Method and sampling strategy will improve benthic monitoring. © 2012 Elsevier Ltd.

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Connectivity mapping is the process of establishing connections between different biological states using gene-expression profiles or signatures. There are a number of applications but in toxicology the most pertinent is for understanding mechanisms of toxicity. In its essence the process involves comparing a query gene signature generated as a result of exposure of a biological system to a chemical to those in a database that have been previously derived. In the ideal situation the query gene-expression signature is characteristic of the event and will be matched to similar events in the database. Key criteria are therefore the means of choosing the signature to be matched and the means by which the match is made. In this article we explore these concepts with examples applicable to toxicology.

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Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

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The analysis of clinical breast samples using biomarkers is integral to current breast cancer management. Currently, a limited number of targeted therapies are standard of care in breast cancer treatment. However, these targeted therapies are only suitable for a subset of patients and resistance may occur. Strategies to prevent the occurrence of invasive lesions are required to reduce the morbidity and mortality associated with the development of cancer. In theory, application of targeted therapies to pre-invasive lesions will prevent their progression to invasive lesions with full malignant potential. The diagnostic challenge for pathologists is to make interpretative decisions on early detected pre-invasive lesions. Overall, only a small proportion of these pre-invasive lesions will progress to invasive carcinoma and morphological assessment is an imprecise and subjective means to differentiate histologically identical lesions with varying malignant potential. Therefore differential biomarker analysis in pre-invasive lesions may prevent overtreatment with surgery and provide a predictive indicator of response to therapy. There follows a review of established and emerging potential druggable targets in pre-invasive lesions and correlation with lesion morphology.

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Objective: Guidelines recommend the creation of a wrist radiocephalic arteriovenous fistula (RAVF) as initial hemodialysis vascular access. This study explored the potential of preoperative ultrasound vessel measurements to predict AVF failure to mature (FTM) in a cohort of patients with end-stage renal disease in Northern Ireland

.Methods: A retrospective analysis was performed of all patients who had preoperative ultrasound mapping of upper limb blood vessels carried out from August 2011 to December 2014 and whose AVF reached a functional outcome by March 2015.

Results: There were 152 patients (97% white) who had ultrasound mapping andan AVF functional outcome recorded; 80 (54%) had an upper arm AVF created, and 69 (46%) had a RAVF formed. Logistic regression revealed that female gender (odds ratio [OR], 2.5; 95% confidence interval [CI], 1.12-5.55; P = .025), minimum venous diameter (OR, 0.6; 95% CI, 0.39-0.95; P = .029), and RAVF (OR, 0.4; 95% CI, 0.18-0.89; P = .026) were associated with FTM. On subgroup analysis of the RAVF group, RAVFs with an arterial volume flow <50 mL/min were seven times as likely to fail as RAVFs with higher volume flows (OR, 7.0; 95% CI, 2.35-20.87; P < .001).

Conclusions: In this cohort, a radial artery flow rate <50 mL/min was associated with a sevenfold increased risk of FTM in RAVF, which to our knowledge has not been previously reported in the literature. Preoperative ultrasound mapping adds objective assessment in the clinical prediction of AVF FTM.