A Fast-Slow Analysis of the Dynamics of REM Sleep

Waking and sleep states are regulated by the coordinated activity of a number of neuronal population in the brainstem and hypothalamus whose synaptic interactions compose a sleep-wake regulatory network. Physiologically based mathematical models of the sleep-wake regulatory network contain mechanisms operating on multiple time scales including relatively fast synaptic-based interations between neuronal populations, and much slower homeostatic and circadian processes that modulate sleep-wake temporal patterning. In this study, we exploit the naturally arising slow time scale of the homeostatic sleep drive in a reduced sleep-wake regulatory network model to utilize fast-slow analysis to investigate the dynamics of rapid eye movement (REM) sleep regulation. The network model consists of a reduced number of wake-, non-REM (NREM) sleep-, and REM sleep-promoting neuronal populations with the synaptic interactions reflecting the mutually inhibitory flip-flop conceptual model for sleep-wake regulation and the reciprocal interaction model for REM sleep regulation. Network dynamics regularly alternate between wake and sleep states as goverend by the slow homeostatic sleep drive. By varying a parameter associated with the activation of the REM-promoting population, we cause REM dynamics during sleep episodes to vary from supression to single activations to regular REM-NREM cycling, corresponding to changes in REM patterning induced by circadian modulation and observed in different mammalian species. We also utilize fast-slow analysis to explain complex effects on sleep-wake patterning of simulated experiments in which agonists and antagonists of different neurotransmitters are microinjected into specific neuronal populations participating in the sleep-wake regulatory network

Supervised Classification Using Sparse Fisher's LDA

It is well known that in a supervised classification setting when the number of features is smaller than the number of observations, Fisher's linear discriminant rule is asymptotically Bayes. However, there are numerous modern applications where classification is needed in the high-dimensional setting. Naive implementation of Fisher's rule in this case fails to provide good results because the sample covariance matrix is singular. Moreover, by constructing a classifier that relies on all features the interpretation of the results is challenging. Our goal is to provide robust classification that relies only on a small subset of important features and accounts for the underlying correlation structure. We apply a lasso-type penalty to the discriminant vector to ensure sparsity of the solution and use a shrinkage type estimator for the covariance matrix. The resulting optimization problem is solved using an iterative coordinate ascent algorithm. Furthermore, we analyze the effect of nonconvexity on the sparsity level of the solution and highlight the difference between the penalized and the constrained versions of the problem. The simulation results show that the proposed method performs favorably in comparison to alternatives. The method is used to classify leukemia patients based on DNA methylation features

Variation in habitat preference and distribution of harbour porpoises west of Scotland

The waters off the west coast of Scotland have one of the highest densities of harbour porpoise (Phocoena phocoena) in Europe. Harbour porpoise are listed under Annex II of the EU Habitats Directive, requiring the designation of Special Areas of Conservation (SACs) for the species’ protection and conservation. The main aim of this thesis is to identify habitat preferences for harbour porpoise, and key regions that embody these preferences, which could therefore be suitable as SACs; and to determine how harbour porpoise use these regions over time and space. Designed visual and acoustic line-transect surveys were conducted between 2003 and 2008. Generalised Estimating Equations (GEEs) were used to determine relationships between the relative density of harbour porpoise and temporally and spatially variable oceanographic covariates. Predictive models showed that depth, slope, distance to land and spring tidal range were all important in explaining porpoise distribution. There were also significant temporal variations in habitat use. However, whilst some variation was observed among years and months, consistent preferences for water depths between 50 and 150 m and highly sloped regions were observed across the temporal models. Predicted surfaces revealed a consistent inshore distribution for the species throughout the west coast of Scotland. Regional models revealed similar habitat preferences to the full-extent models, and indicated that the Small Isles and Sound of Jura were the most consistently important regions for harbour porpoise, and that these regions could be suitable as SACs. The impacts of seal scarers on distribution and habitat use were also investigated, and there were indications that these devices have the potential to displace harbour porpoise. These results should be considered in the assessment of sites for SAC designation, and in implementing appropriate conservation measures for harbour porpoise

Nurses’ Learning and Conceptualization of Technology used in Practice

How nurses conceptualize and learn about health technology used in practice was examined in this qualitative, interpretive-descriptive study. Traditionally, conceptualizations of technology used in the nursing profession have been viewed from either socially- or technically- centric perspectives that have clouded the real nature of nurse-technology interactions. For instance, current perspectives examining nurses’ use of technology typically ignore or minimize socio-technical considerations impacting technology acceptance and adoption by nurses. A research approach that embraced the mingling of social and material (sociomaterial) actors was used to address the following research questions: (a) How do nurses conceptualize health technology used in practice?, and, (b) How do nurses learn about health technology used in practice? The theoretical lens of Actor-Network Theory (ANT) provided the overall perspective and guided elements of data collection and analysis. ANT is aligned to a relational ontology, whereby both human and non-human participants (or actors) are viewed in symmetry (or as equals) during data analysis. Privilege during the analysis was, therefore, not automatically prescribed to either the human or non-human actors. Interviews, documents, and direct observation of nurses constituted the majority of the data collected for this study. Using an iterative data analysis process, themes were generated related to nurses’ conceptualization of and learning about technology used in practice. Technology was conceptualized by nurses to possess variation in naming, roles, and also engendered notions of action or praxis. Learning technology by nurses possessed elements resembling both processes and products. From these learning processes and products, salient strategies (e.g., indispensability, semblance, habituation) were developed by nurses in order to negotiate and use various health technologies for practice. Ultimately, learning of health technology by nurses appeared to actively influence, modify, and shape the role of health technology, and its subsequent use by human actors. Therefore, how nurses learn about technology should be considered during the planning, development, and evaluation of future technologies. End-users, like nurses, will rarely use a health technology to its fullest capability unless learning is congruent with the environmental context surrounding the technological actor. In light of these findings, recommendations for nursing education and professional practice related to the role and interpretation of health technology used by nurses in 2013 is also discussed, along with implications for future research