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Gaucher disease (GD), the most frequent lysosomal storage disease, is caused by heterogeneous mutations in the locus coding for glucocerebrosidase (GBA). It is an autosomal recessive disorder with different phenotypes of which the most frequent is the nonneuronopathic or type 1, prevalent worldwide. To date, more than 430 mutations have been described, but their frequency distribution varies in different populations with four, N370S, L444P, IVS2 + 1G > A and 84insG, being the most frequent ones. In Venezuela, 20 unrelated index cases with GD type I were assessed for GBA mutation detection and for their in-phase haplotype identification, to gather genetic epidemiological data on the disease in the country and of its eventual ethnic origin. Ten missense mutations and two complex alleles were identified. The most frequent were N370S (42.5%), L444P (20%), IVS2+1G > A (10%) and R48W (5%); mutations R120W, P245H, H311R, R496H, W36X and R433G which were carried by a single chromosome each one. Three geographical foci were identified, displaying mutation heterogeneity. N370S had multiple genetic origins, different from the Ashkenazi's; a single common remote ancestor for this mutation in the country was dismissed, according to the haplotype analysis. All mutations have a likely European Caucasoid descent.

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This chapter tailors a perspective of the work fulfilled in three learning research lines, which besides holding many common attributes also tend to converge to shape mobile, ubiquitous, and pervasive sceneries. Such a junction pursues to spread the traditional classroom and distance settings to open environments, as well as use the surrounding physical and digital objects as learning content that is available to learners at anytime, anywhere, and in any way. In sum, a complete learning environment is recreated to provide formal and informal learning to support academic studies, professional training, and lifelong learning. Thus, in this chapter a description of the mobile, ubiquitous, and pervasive learning (MUP-Learning) arena is presented through the selection of a sample of recent and transcendent works that offer from a conceptual contribution, such as models and frameworks, even empirical approaches oriented to specific domains of study. The sample of works is characterized according to a proposed pattern, as well as organized according to a suggested taxonomy. A profile to describe each work is also stated and a series of statistics are presented, as well as an analysis of the arena is provided to understand the potential and challenges related to the MUP-Learning field.