Background: Over the past decade, the use of electronic nicotine delivery system (ENDS) devices such as ecigarettes has increased dramatically, particularly among students and other transitional-age youth. Societal norms and the variety of ENDS devices available have also evolved dramatically in recent years. Objective. This article provides a comprehensive review and synthesis of contemporary literature, as it relates to ENDS use among transitional age youth. Method: Over 125 peer reviewed studies, literature syntheses, legal reports and contemporary media works focused on ENDS use and vaping were reviewed. Results: Marketing strategies for ENDS devices have primarily targeted teens and young adults. Though ENDS devices are advertised as a safer alternative to cigarettes, accumulating data demonstrate significant health risks and consequences associated with use. The long-term health effects remain largely unknown, however detrimental acute effects are apparent. Further, rather that aiding in tobacco cessation efforts, use of ENDS by transitional age youth is correlated with increased use of conventional tobacco products and other substances of abuse. Students appear to be ill-informed regarding the dangers of using ENDS products. Conclusion: Given the rapid increase in ENDS users each year, and accumulating concerns about health risks associated with use, university student health services must be prepared to address this growing problem. As clinical practice guidelines do not yet exist to encourage ENDS-product cessation, use of the evidence-based strategies developed for tobacco cessation are advised. More research is needed to determine the most effective methods to prevent initiation of ENDS use within this population.
Background: Neuroimaging is an important tool in early detection of Alzheimer’s disease (AD) which is a serious neurodegenerative brain disease among the elderly subjects. Independent component analysis (ICA) is arguably one of the most widely used algorithm for the analysis of brain imaging data, which can be used to extract intrinsic networks of brain from functional magnetic resonance imaging (fMRI). Method: Witnessed by recent studies, a more flexible model known as restricted Boltzmann machine (RBM) can also be used to extract spatial maps and time courses of intrinsic networks from resting state fMRI, moreover, RBM shows superior temporal features than ICA. Here, we seek to employ RBM to improve performance of classifying individuals. Experiments are performed on healthy controls and subjects at early stage of AD, i.e., cognitive normal (CN) and early mild cognitive impairment participants (EMCI), and two types of data, i.e., structural magnetic resonance imaging (sMRI) and fMRI data. Results: (1) By separately employing ICA for sMRI and fMRI, the features extracted from fMRI improve classification accuracy by 7.5% for CN and EMCI; (2) instead of applying ICA to fMRI, using RBM further improves classification accuracy by 7.75% for CN and EMCI; (3) the lesions at early stage of AD are more likely to occur in the regions around slices 4, 6, 10, 14, 19, 51 and 59 of the whole brain in the longitudinal direction. Conclusion: By using fMRI instead of sMRI and RBM instead of ICA, we can classify CN and EMCI more efficient.
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