The Northern Ireland Action Plan for Learning Disability Nursing

The Northern Ireland Action Plan for Learning Disability Nursing

Learning Disability Service Framework (PDF 3MB)

Learning Disability Service Framework - Easy Access Version

Learning Disability Service Framework

Easy Access Version

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)

A Workforce Learning Strategy for the Northern Ireland Health and Social Care Services 2009-2014

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.

Extracellular matrix and platelet function in patients with musculocontractural Ehlers-Danlos syndrome caused by mutations in the CHST14 gene.

We report on a consanguineous, Afghani family with two sisters affected with characteristic facial features, multiple contractures, progressive joint and skin laxity, hemorrhagic diathesis following minor trauma and multisystem fragility-related manifestations suggestive of a diagnosis of musculocontractural Ehlers-Danlos syndrome (EDS). This novel form of connective tissue disorder was recently reported in patients of Japanese, Turkish, and Indian descent who were formerly classified as having EDS type VIB and has now been recognized to be a part of spectrum including patients previously classified as having adducted thumb-clubfoot syndrome. We identified a previously unreported mutation in the CHST14 gene, which codes for the enzyme dermatan 4-O-sulfotransferase. We discuss the prenatal presentation, detailed clinical manifestations, and neurological findings in two sisters with this newly described musculocontractural EDS-CHST14 type. We demonstrate that fibroblasts from one of our patients produce more chondroitin sulfate than normal and show lower than normal deposition of collagens I and II and fibrillin 1-containing microfibrills. These findings suggest that the imbalance in the glycosaminoglycan content in developing tissues might interfere with normal deposition of other extracellular matrix components and ultimately contribute to the development of the phenotype observed in these patients. Furthermore, we ruled out the contribution of intrinsic platelet factors to the bleeding diathesis observed in some affected individuals. © 2012 Wiley Periodicals, Inc.

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.