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

Properties of the mechanosensitive channel MscS pore revealed by tryptophan scanning mutagenesis

Funding This work was supported by a Wellcome Trust Programme grant [092552/A/10/Z awarded to I.R.B., S.M., J. H. Naismith (University of St Andrews, St Andrews, U.K.), and S. J. Conway (University of Oxford, Oxford, U.K.)] (T.R. and M.D.E.), by a BBSRC grant (A.R.) [BB/H017917/1 awarded to I.R.B., J. H. Naismith, and O. Schiemann (University of St Andrews)], by a Leverhulme Emeritus Fellowship (EM-2012-060\2), and by a CEMI grant to I.R.B. from the California Institute of Technology. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013 FP7/2007-2011) under Grant PITN-GA-2011-289384 (FP7-PEOPLE-2011-ITN NICHE) (H.G.) (awarded to S.M.).Peer reviewedPublisher PD

Integrative Model-based clustering of microarray methylation and expression data

In many fields, researchers are interested in large and complex biological processes. Two important examples are gene expression and DNA methylation in genetics. One key problem is to identify aberrant patterns of these processes and discover biologically distinct groups. In this article we develop a model-based method for clustering such data. The basis of our method involves the construction of a likelihood for any given partition of the subjects. We introduce cluster specific latent indicators that, along with some standard assumptions, impose a specific mixture distribution on each cluster. Estimation is carried out using the EM algorithm. The methods extend naturally to multiple data types of a similar nature, which leads to an integrated analysis over multiple data platforms, resulting in higher discriminating power.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS533 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Mortality in intensive care: The impact of bacteremia and the utility of systemic inflammatory response syndrome

Background: The purpose of this study was to determine the impact of bacteremia on intensive care unit (ICU) mortality and to develop a bacteremia prediction tool using systemic inflammatory response syndrome (SIRS) criteria. Methods: Patients included those aged >18 years who had blood cultures taken in the ICU from January 1, 2011-December 31, 2013. Eligible patients were identified from microbiology records of the Glasgow Royal Infirmary, Scotland. Clinical and outcome data were gathered from ICU records. Patients with clinically significant bacteremia were matched to controls using propensity scores. SIRS criteria were gathered and used to create decision rules to predict the absence of bacteremia. The main outcome was mortality at ICU discharge. The utility of the decision tools was measured using sensitivity and specificity. Results: One hundred patients had a clinically significant positive blood culture and were matched to 100 controls. Patients with bacteremia had higher ICU mortality (odds ratio [OR], 2.35; P = .001) and longer ICU stay (OR, 17.0 vs 7.8 days; P ≤ .001). Of 1,548 blood culture episodes, 1,274 met ≥2 SIRS criteria (106 significant positive cultures and 1,168 negative cultures). There was no association between SIRS criteria and positive blood cultures (P = .11). A decision rule using 3 SIRS criteria had optimal predictive performance (sensitivity, 56%; specificity, 50%) but low accuracy. Conclusions: ICU patients with bacteremia have increased mortality and length of ICU stay. SIRS criteria cannot be used to identify patients at low risk of bacteremia