RQIA Review of the implementation of Promoting Quality Care (PQC) Good Practice Guidance on the Assessment and Management of Risk in Mental Health and Learning Disability Services (PDF1.07MB)

Overview Report October 2012

A Workforce Learning Strategy for the Northern Ireland Health and Social Care Services 2009-2014 (PDF 318KB)

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Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Characterization of the Aspergillus nidulans biotin biosynthetic gene cluster and use of the bioDA gene as a new transformation marker.

The genes involved in the biosynthesis of biotin were identified in the hyphal fungus Aspergillus nidulans through homology searches and complementation of Escherichia coli biotin-auxotrophic mutants. Whereas the 7,8-diaminopelargonic acid synthase and dethiobiotin synthetase are encoded by distinct genes in bacteria and the yeast Saccharomyces cerevisiae, both activities are performed in A. nidulans by a single enzyme, encoded by the bifunctional gene bioDA. Such a bifunctional bioDA gene is a genetic feature common to numerous members of the ascomycete filamentous fungi and basidiomycetes, as well as in plants and oömycota. However, unlike in other eukaryota, the three bio genes contributing to the four enzymatic steps from pimeloyl-CoA to biotin are organized in a gene cluster in pezizomycotina. The A. nidulans auxotrophic mutants biA1, biA2 and biA3 were all found to have mutations in the 7,8-diaminopelargonic acid synthase domain of the bioDA gene. Although biotin auxotrophy is an inconvenient marker in classical genetic manipulations due to cross-feeding of biotin, transformation of the biA1 mutant with the bioDA gene from either A. nidulans or Aspergillus fumigatus led to the recovery of well-defined biotin-prototrophic colonies. The usefulness of bioDA gene as a novel and robust transformation marker was demonstrated in co-transformation experiments with a green fluorescent protein reporter, and in the efficient deletion of the laccase (yA) gene via homologous recombination in a mutant lacking non-homologous end-joining activity.

Activity in inferior parietal and medial prefrontal cortex signals the accumulation of evidence in a probability learning task.

In an uncertain environment, probabilities are key to predicting future events and making adaptive choices. However, little is known about how humans learn such probabilities and where and how they are encoded in the brain, especially when they concern more than two outcomes. During functional magnetic resonance imaging (fMRI), young adults learned the probabilities of uncertain stimuli through repetitive sampling. Stimuli represented payoffs and participants had to predict their occurrence to maximize their earnings. Choices indicated loss and risk aversion but unbiased estimation of probabilities. BOLD response in medial prefrontal cortex and angular gyri increased linearly with the probability of the currently observed stimulus, untainted by its value. Connectivity analyses during rest and task revealed that these regions belonged to the default mode network. The activation of past outcomes in memory is evoked as a possible mechanism to explain the engagement of the default mode network in probability learning. A BOLD response relating to value was detected only at decision time, mainly in striatum. It is concluded that activity in inferior parietal and medial prefrontal cortex reflects the amount of evidence accumulated in favor of competing and uncertain outcomes.