Safe Therapeutic Economic Pharmaceutical Selection (STEPSelect): development, introduction and use in Northern Ireland

The implementation of a new model for drug selection in Northern Ireland has improved the quality of prescribing, safety and reduced costs.

Patient selection for oncology phase I trials: a multi-institutional study of prognostic factors.

PURPOSE The appropriate selection of patients for early clinical trials presents a major challenge. Previous analyses focusing on this problem were limited by small size and by interpractice heterogeneity. This study aims to define prognostic factors to guide risk-benefit assessments by using a large patient database from multiple phase I trials. PATIENTS AND METHODS Data were collected from 2,182 eligible patients treated in phase I trials between 2005 and 2007 in 14 European institutions. We derived and validated independent prognostic factors for 90-day mortality by using multivariate logistic regression analysis. Results The 90-day mortality was 16.5% with a drug-related death rate of 0.4%. Trial discontinuation within 3 weeks occurred in 14% of patients primarily because of disease progression. Eight different prognostic variables for 90-day mortality were validated: performance status (PS), albumin, lactate dehydrogenase, alkaline phosphatase, number of metastatic sites, clinical tumor growth rate, lymphocytes, and WBC. Two different models of prognostic scores for 90-day mortality were generated by using these factors, including or excluding PS; both achieved specificities of more than 85% and sensitivities of approximately 50% when using a score cutoff of 5 or higher. These models were not superior to the previously published Royal Marsden Hospital score in their ability to predict 90-day mortality. CONCLUSION Patient selection using any of these prognostic scores will reduce non-drug-related 90-day mortality among patients enrolled in phase I trials by 50%. However, this can be achieved only by an overall reduction in recruitment to phase I studies of 20%, more than half of whom would in fact have survived beyond 90 days.

Ecological coherence in marine reserve network design: an empirical evaluation of sequential site selection using genetic structure

Ecological coherence is a multifaceted conservation objective that includes some potentially conflicting concepts. These concepts include the extent to which the network maximises diversity (including genetic diversity) and the extent to which protected areas interact with non-reserve locations. To examine the consequences of different selection criteria, the preferred location to complement protected sites was examined using samples taken from four locations around each of two marine protected areas: Strangford Lough and Lough Hyne, Ireland. Three different measures of genetic distance were used: FST, Dest and a measure of allelic dissimilarity, along with a direct assessment of the total number of alleles in different candidate networks. Standardized site scores were used for comparisons across methods and selection criteria. The average score for Castlehaven, a site relatively close to Lough Hyne, was highest, implying that this site would capture the most genetic diversity while ensuring highest degree of interaction between protected and unprotected sites. Patterns around Strangford Lough were more ambiguous, potentially reflecting the weaker genetic structure around this protected area in comparison to Lough Hyne. Similar patterns were found across species with different dispersal capacities, indicating that methods based on genetic distance could be used to help maximise ecological coherence in reserve networks. ⺠Ecological coherence is a key component of marine protected area network design. ⺠Coherence contains a number of competing concepts. ⺠Genetic information from field populations can help guide assessments of coherence. ⺠Average choice across different concepts of coherence was consistent among species. ⺠Measures can be combined to compare the coherence of different network designs.

Functional responses of the intertidal amphipod Echinogammarus marinus: effects of prey supply, model selection and habitat complexity.

The influence of predation in structuring ecological communities can be informed by examining the shape and magnitude of the functional response of predators towards prey. We derived functional responses of the ubiquitous intertidal amphipod Echinogammarus marinus towards one of its preferred prey species, the isopod Jaera nordmanni. First, we examined the form of the functional response where prey were replaced following consumption, as compared to the usual experimental design where prey density in each replicate is allowed to deplete. E. marinus exhibited Type II functional responses, i.e. inversely density-dependent predation of J. nordmanni that increased linearly with prey availability at low densities, but decreased with further prey supply. In both prey replacement and non-replacement experiments, handling times and maximum feeding rates were similar. The non-replacement design underestimated attack rates compared to when prey were replaced. We then compared the use of Holling’s disc equation (assuming constant prey density) with the more appropriate Rogers’ random predator equation (accounting for prey depletion) using the prey non-replacement data. Rogers’ equation returned significantly greater attack rates but lower maximum feeding rates, indicating that model choice has significant implications for parameter estimates. We then manipulated habitat complexity and found significantly reduced predation by the amphipod in complex as opposed to simple habitat structure. Further, the functional response changed from a Type II in simple habitats to a sigmoidal, density-dependent Type III response in complex habitats, which may impart stability on the predator−prey interaction. Enhanced habitat complexity returned significantly lower attack rates, higher handling times and lower maximum feeding rates. These findings illustrate the sensitivity of the functional response to variations in prey supply, model selection and habitat complexity and, further, that E. marinus could potentially determine the local exclusion and persistence of prey through habitat-mediated changes in its predatory functional responses.

AdaBoost Multiple Feature Selection and Combination for Face Recognition

Gabor features have been recognized as one of the most successful face representations. Encouraged by the results given by this approach, other kind of facial representations based on Steerable Gaussian first order kernels and Harris corner detector are proposed in this paper. In order to reduce the high dimensional feature space, PCA and LDA techniques are employed. Once the features have been extracted, AdaBoost learning algorithm is used to select and combine the most representative features. The experimental results on XM2VTS database show an encouraging recognition rate, showing an important improvement with respect to face descriptors only based on Gabor filters.

Variable selection via RIVAL and its application in nuclear material detection

In many situations, the number of data points is fixed, and the asymptotic convergence results of popular model selection tools may not be useful. A new algorithm for model selection, RIVAL (removing irrelevant variables amidst Lasso iterations), is presented and shown to be particularly effective for a large but fixed number of data points. The algorithm is motivated by an application of nuclear material detection where all unknown parameters are to be non-negative. Thus, positive Lasso and its variants are analyzed. Then, RIVAL is proposed and is shown to have some desirable properties, namely the number of data points needed to have convergence is smaller than existing methods.