992 resultados para Learning disorders


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The Northern Ireland Action Plan for Learning Disability Nursing

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Learning Disability Service Framework - Easy Access Version

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Easy Access Version

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

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Optimum management of non-acquired neuromuscular disorders requires a multidisciplinary approach in order to prevent secondary complications related to the progression of the disease and to maintain the patient's independency in daily activities. For treatments, the physiotherapists and occupational therapists must have precise and measurable goals to quantify muscle strength and functions in conjunction with a specialist in neurorehabilitation. Examples of simple motor scores or scales are given in order to transmit precise information to the GP and the multidisciplinary team, and type of orthosis and physiotherapy programmes are given as pieces of advice to assume the follow-up of patients.

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Combination antiretroviral therapy has dramatically decreased the incidence of HIV-related mortality and serious opportunistic diseases, among which is HIV- associated dementia. However, minor forms of cognitive dysfunction have not disappeared and may even have increased in frequency. Aging of HIV+ patients, insufficient penetration of antiretroviral drugs into the brain with continuous low- grade viral production and inflammation may play a role. A putative neurotoxicity of combination antiretroviral therapy is controversial. In this article, we will discuss these aspects, as well as clinical and pathophysiological features shared by HIV-associated neurocognitive disorders and other neurodegenerative diseases, especially Alzheimer's disease. This article will briefly summarize the current clinical trials on neuroprotective agents, and the management of patients with neurocognitive disorders will be discussed

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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.