The Role of Phenotyping in the Personalised Management of OSA

Background: Obstructive sleep apnoea (OSA) is estimated to affect up to 1 billion people in the world. Those who fail first-line continuous positive airway pressure (CPAP) therapy have salvage treatment options available. Patient assessment can incorporate multidisciplinary teams to better select therapy. Traditional parameters that define OSA severity do not always correlate with symptoms of the disease. Newly identified pathophysiological “phenotypes” of airway vulnerability, low arousal threshold, loop gain and muscle responsiveness may explain the heterogeneity of OSA for up to two-thirds of patients. Little data exists on the effectiveness of phenotyping in a real-world clinical setting for patients undergoing contemporary management paradigms. Aims and Hypothesis: To evaluate the prevalence of the four OSA phenotypic traits and explore the clinical validity of endotyping in predicting future treatment outcomes. It is expected that non-responders to treatment will have unfavourable non-anatomical phenotypes. Design: An observational prospective cohort study of 49 patients referred after failure of CPAP for consideration of salvage therapy was conducted. Treatments included upper airway surgery (n = 17), mandibular advancement splint (n = 7), positional therapy (n = 7), weight loss (n = 4), nerve stimulation (n = 5) and combination therapy (n = 9). Treatment “success” was defined using polysomnographic parameters and patient-reported outcome measures of sleepiness and function. Phenotypic traits were analysed according to these outcomes. Results: Nearly all surgical patients had unfavourable loop gain (LG1 \u3e 0.72), which improved after surgical treatment (p \u3c .05). Patients who had decreased sleepiness (Epworth Sleepiness Scale reduction ≥ 3, total score \u3c 10, p = .01) after any treatment had favourable traits of low loop gain, lower arousal threshold and lower muscle compensation. There may be a potential role for phenotyping in predicting expected outcomes from salvage treatment for OSA, although more prospective clinical data is required to further investigate its utility and relevance

A New Mechanism for Bubble Nucleation: Classical Transitions

Given a scalar field with metastable minima, bubbles nucleate quantum mechanically. When bubbles collide, energy stored in the bubble walls is converted into kinetic energy of the field. This kinetic energy can facilitate the classical nucleation of new bubbles in minima that lie below those of the "parent" bubbles. This process is efficient and classical, and changes the dynamics and statistics of bubble formation in models with multiple vacua, relative to that derived from quantum tunneling.Comment: 4 pages, 4 figures, animations related to figures can be found at http://www.perimeterinstitute.ca/personal/jgiblin/BubbleMovies.htm

How to Run Through Walls: Dynamics of Bubble and Soliton Collisions

It has recently been shown in high resolution numerical simulations that relativistic collisions of bubbles in the context of a multi-vacua potential may lead to the creation of bubbles in a new vacuum. In this paper, we show that scalar fields with only potential interactions behave like free fields during high-speed collisions; the kick received by them in a collision can be deduced simply by a linear superposition of the bubble wall profiles. This process is equivalent to the scattering of solitons in 1+1 dimensions. We deduce an expression for the field excursion (shortly after a collision), which is related simply to the field difference between the parent and bubble vacua, i.e. contrary to expectations, the excursion cannot be made arbitrarily large by raising the collision energy. There is however a minimum energy threshold for this excursion to be realized. We verify these predictions using a number of 3+1 and 1+1 numerical simulations. A rich phenomenology follows from these collision induced excursions - they provide a new mechanism for scanning the landscape, they might end/begin inflation, and they might constitute our very own big bang, leaving behind a potentially observable anisotropy.Comment: 15pgs, 14 figures, v2, thanks for the feedback

On Estimating the QSO Transmission Power Spectrum

We present new methods to minimize the systematic and random errors for measuring the transmission power spectrum from the Lyman-alpha forest. Sources of systematic errors explored include metal line contamination and continuum-fitting. We advocate the technique of trend-removal in place of traditional continuum-fitting -- here, a spectrum is normalized by its (smoothly varying) mean rather than its continuum -- this method is easily automated and removes biases introduced by continuum-fitting. Trend- removal can be easily applied to spectra where continuum-fitting is difficult, such as when the resolution or signal-to-noise is low, or for spectra at high redshifts. Furthermore, a measurement of the continuum power spectrum using trend-removal, from either low redshift quasar spectra or the red-side of Lyman-alpha, allows in principle the removal of spurious power introduced by the continuum and thereby expanding scales probed to larger ones. We also derive expressions for the shot-noise bias and variance of the power spectrum estimate, taking into account the non-Poissonian nature of the shot-noise and the non-Gaussianity of the cosmic fluctuations. An appropriate minimum variance weighting of the data is given. Finally, we give practical suggestions on observing strategy: the desired resolution and S/N for different purposes, and how to distribute one's finite observing time among quasar targets. Also discussed is the quasar spectroscopic study of the Sloan Digital Sky Survey, which has the potential to measure the power spectrum at z ~ 2-4 accurate to better than 1 % per mode -- the techniques presented here will be useful for tackling the anticipated issues of shot-noise and continuum contamination.Comment: 35 pages, 14 figures, submitted to Ap

Neurochemical correlates of autistic disorder: A review of the literature

Review of neurochemical investigations in autistic disorder revealed that a wide array of transmitter systems have been studied, including serotonin, dopamine, norepinephrine, acetylcholine, oxytocin, endogenous opioids, cortisol, glutamate, and gamma-aminobutyric acid (GABA). These studies have been complicated by the fact that autism is a very heterogeneous disorder which often presents with comorbid behavioral problems. In addition, many of these studies employed very small samples and inappropriate control groups, making it difficult to draw conclusions with confidence. Overall, serotonin appears to have the most empirical evidence for a role in autism, but this requires further investigation and replication. There is little support for the notion that a dysfunction of norepinephrine or the endogenous opioids are related to autism. The role of dopaminergic functioning has not been compelling thus far, though conflicting findings on central dopamine turnover require further study. Promising new areas of study may include possible dysfunction of the cholinergic system, oxytocin, and amino acid neurotransmitters. Implications for pharmacotherapy are briefly discussed for each neurotransmitter system with brief research examples. Review of this work emphasizes the need for future studies to control for subject variables, such as race, sex, pubertal status, and distress associated with blood draws, which can affect measures of neurochemical function. In addition, research in neurochemistry must continue to work in concert with other subspecialties to form a more comprehensive and theory-based approach to the neurobiological correlates of autistic disorder

Neurochemical correlates of autistic disorder: A review of the literature

Review of neurochemical investigations in autistic disorder revealed that a wide array of transmitter systems have been studied, including serotonin, dopamine, norepinephrine, acetylcholine, oxytocin, endogenous opioids, cortisol, glutamate, and gamma-aminobutyric acid (GABA). These studies have been complicated by the fact that autism is a very heterogeneous disorder which often presents with comorbid behavioral problems. In addition, many of these studies employed very small samples and inappropriate control groups, making it difficult to draw conclusions with confidence. Overall, serotonin appears to have the most empirical evidence for a role in autism, but this requires further investigation and replication. There is little support for the notion that a dysfunction of norepinephrine or the endogenous opioids are related to autism. The role of dopaminergic functioning has not been compelling thus far, though conflicting findings on central dopamine turnover require further study. Promising new areas of study may include possible dysfunction of the cholinergic system, oxytocin, and amino acid neurotransmitters. Implications for pharmacotherapy are briefly discussed for each neurotransmitter system with brief research examples. Review of this work emphasizes the need for future studies to control for subject variables, such as race, sex, pubertal status, and distress associated with blood draws, which can affect measures of neurochemical function. In addition, research in neurochemistry must continue to work in concert with other subspecialties to form a more comprehensive and theory-based approach to the neurobiological correlates of autistic disorder

Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes

We investigate how simultaneously recorded long-range power-law correlated multi-variate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time long-range cross-correlated. We study how the degree of cross-correlations between these signals depends on the scaling exponents characterizing the fractal correlations in each signal and on the coupling between the signals. Our findings have relevance when studying parallel outputs of multiple-component of physical, physiological and social systems.Comment: 8 pages, 5 figures, elsart.cl

Chembench: A Publicly Accessible, Integrated Cheminformatics Portal

The enormous increase in the amount of publicly available chemical genomics data and the growing emphasis on data sharing and open science mandates that cheminformaticians also make their models publicly available for broad use by the scientific community. Chembench is one of the first publicly accessible, integrated cheminformatics Web portals. It has been extensively used by researchers from different fields for curation, visualization, analysis, and modeling of chemogenomics data. Since its launch in 2008, Chembench has been accessed more than 1 million times by more than 5000 users from a total of 98 countries. We report on the recent updates and improvements that increase the simplicity of use, computational efficiency, accuracy, and accessibility of a broad range of tools and services for computer-assisted drug design and computational toxicology available on Chembench. Chembench remains freely accessible at https://chembench.mml.unc.ed

Genome maps across 26 human populations reveal population-specific patterns of structural variation.

Large structural variants (SVs) in the human genome are difficult to detect and study by conventional sequencing technologies. With long-range genome analysis platforms, such as optical mapping, one can identify large SVs (>2 kb) across the genome in one experiment. Analyzing optical genome maps of 154 individuals from the 26 populations sequenced in the 1000 Genomes Project, we find that phylogenetic population patterns of large SVs are similar to those of single nucleotide variations in 86% of the human genome, while ~2% of the genome has high structural complexity. We are able to characterize SVs in many intractable regions of the genome, including segmental duplications and subtelomeric, pericentromeric, and acrocentric areas. In addition, we discover ~60 Mb of non-redundant genome content missing in the reference genome sequence assembly. Our results highlight the need for a comprehensive set of alternate haplotypes from different populations to represent SV patterns in the genome