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Sleep loss is a widespread problem with serious physical and economic consequences. Music can impact upon physical, psychological and emotional states, which may explain anecdotal reports of its success as an everyday sleep aid. However, there is a lack of systematic data on how widely it is used, why people opt for music as a sleep aid, or what music works; hence the underlying drivers to music-sleep effects remain unclear. We investigated music as a sleep aid within the general public via a mixed methods data online survey (n = 651) that scored musicality, sleep habits, and open text responses on what music helps sleep and why. In total, 62% of respondents stated that they used music to help them sleep. They reported fourteen musical genres comprising 545 artists. Linear modelling found stress, age, and music use as significant predictors of sleep quality (PSQI) scores. Regression tree modelling revealed that younger people with higher musical engagement were significantly more likely to use music to aid sleep. Thematic analysis of the open text responses generated four themes that described why people believe music helps sleep: music offers unique properties that stimulate sleep (Provide), music is part of a normal sleep routine (Habit), music induces a physical or mental state conducive to sleep (State), and music blocks an internal or external stimulus that would otherwise disrupt sleep (Distract). This survey provides new evidence into the relationship between music and sleep in a population that ranged widely in age, musicality, sleep habits and stress levels. In particular, the results highlight the varied pathways of effect between music and sleep. Diversity was observed both in music choices, which reflected idiosyncratic preferences rather than any consistent musical structure, and in the reasons why music supports good sleep, which went far beyond simple physical/mental relaxation. [ABSTRACT FROM AUTHOR]

Objectives: The goal of this research was to evaluate how material curl, package structure and handling of pouches containing medical devices affect rates of contact between non-sterile surfaces and sterile devices during aseptic transfer. Methods: One hundred and thirty-six individuals with practical experience in aseptic technique were recruited. Participants were asked to present the contents of four different pouch designs (a standard, one designed to curl in, another to curl out and one that incorporated a tab) using two transfer techniques. During the first block of trials “standard technique” was used; participants presented using their typical methods to the sterile field. Trials in the second block employed “modified technique”; participants were instructed to grab the package at the top center and present package contents using a single, fluid motion. The outside of the pouch and the backs of the participants’ hands were coated using a simulated contaminant before each trial. The simulant was undetectable in the visible spectrum, but fluoresced under a black light. The dependent variable was recorded in a binary fashion and analyzed using a generalized linear mixed model. Results: Participants were between 20–57 and the averaged year 5.1 years of experience in aseptic technique. The data analysis was based on generalized linear mixed effects (GLMM) model, which accommodates the repeated measurements within the same participant. The effect of the pouch design was significant (P‹0.001), but the effect of aseptic technique did not suggest significance (P = 0.088). Specifically, pouches designed with the material curled outward resulted in significantly fewer contacts with non-sterile surfaces than the other styles, including the inward, tab, and standard styles; this was true regardless of the used aseptic technique, standard (P = 0.0171, P = 0.0466, P = 0.0061, respectively) or modified (P‹0.0001 for all comparisons)). Conclusion: Results presented here contribute to a growing body of knowledge that investigates packaging as a potential route of contamination for sterile devices during aseptic presentation. Specifically, we provide insights regarding how both package design and opening technique can be informed in ways that build safety into the healthcare system. [ABSTRACT FROM AUTHOR]

Background: Many studies have indicated the detrimental effect of ambient ozone to respiratory health in different countries. The levels of ozone in Hanoi, Vietnam are frequently above the WHO guideline but very few studies on the effects of ambient ozone on human health have been conducted in this location. This study aimed to examine the effects of ozone on hospital admission for respiratory diseases in Hanoi, by diseases, ages and seasons. Methods: Hospital admissions, air pollutants and meteorological data were collected from January 2010 to June 2014. We used generalized linear models and distributed lag linear model to assess the association. In addition to full year analysis, we conducted restricted analysis of the data for two summer (from June-August) and winter (from December-February) seasons and grouped hospital admissions by diseases and ages (all ages, children 0 to 5 years and elderly >65 years). The delayed effect of ozone was assessed using lags of up to 5 days. Results: Ozone has a stronger effect on the risk of hospital admission for respiratory diseases and wheeze-associated disorders in the winter. For respiratory diseases, children were affected by ozone more than other age groups in both winter and summer. Each increase of 10 μg/m3 of ozone is associated with an increase of 6.2% risk of admission for respiratory disease among children in the winter and 1.2% in the summer. For wheeze-associated disorders, the elderly group seemed to be more affected by ozone in full year and winter but no significant association was found between ozone and admission for wheeze-associated diseases in any age group. Conclusions: Ozone is a risk factor for respiratory admission, especially amongst children under 5 years old in Hanoi, and ozone has a stronger effect in the winter than in the summer in this city. [ABSTRACT FROM AUTHOR]

Assessing geographic patterns of species richness is essential to develop biological conservation as well as to understand the processes that shape these patterns. We aim to improve geographic prediction of tree species richness (TSR) across eastern USA by using: 1) gridded point-sample data rather than spatially generalized range maps for the TSR outcome variable, 2) new predictor variables (forest area FA; mean frost day frequency MFDF) and 3) regression models that account for spatial autocorrelation. TSR was estimated in 50 km by 50 km grids using Forest Inventory and Analysis (FIA) point-sample data. Eighteen environmental predictor variables were employed, with the most effective set selected by a LASSO that reduced multicollinearity. Those predictors were then employed in Generalized linear models (GLMs), and in Eigenvector spatial filtering (ESF) models that accounted for spatial autocorrelation. Models were evaluated by model fit statistics, spatial patterns of TSR predictions, and spatial autocorrelation. Our results showed gridded TSR was best-predicted by the ESF model that used, in descending order of influence: precipitation seasonality, mean precipitation in the driest quarter, FA, and MFDF. ESF models, by accounting for spatial autocorrelation, outperformed GLMs regardless of the predictors employed, as indicated by percent deviance explained and spatial autocorrelation of residuals. Small regions with low TSR, such as the Midwest prairie peninsula, were successfully predicted by ESF models, but not by GLMs or other studies. Gridded TSR in Florida was only correctly predicted by the ESF model with FA and MFDF, and was over-predicted by all other models. [ABSTRACT FROM AUTHOR]

Asthma and chronic obstructive pulmonary disease (COPD) patients are required to inhale forcefully and deeply to receive medication when using a dry powder inhaler (DPI). There is a clinical need to objectively monitor the inhalation flow profile of DPIs in order to remotely monitor patient inhalation technique. Audio-based methods have been previously employed to accurately estimate flow parameters such as the peak inspiratory flow rate of inhalations, however, these methods required multiple calibration inhalation audio recordings. In this study, an audio-based method is presented that accurately estimates inhalation flow profile using only one calibration inhalation audio recording. Twenty healthy participants were asked to perform 15 inhalations through a placebo Ellipta™ DPI at a range of inspiratory flow rates. Inhalation flow signals were recorded using a pneumotachograph spirometer while inhalation audio signals were recorded simultaneously using the Inhaler Compliance Assessment device attached to the inhaler. The acoustic (amplitude) envelope was estimated from each inhalation audio signal. Using only one recording, linear and power law regression models were employed to determine which model best described the relationship between the inhalation acoustic envelope and flow signal. Each model was then employed to estimate the flow signals of the remaining 14 inhalation audio recordings. This process repeated until each of the 15 recordings were employed to calibrate single models while testing on the remaining 14 recordings. It was observed that power law models generated the highest average flow estimation accuracy across all participants (90.89±0.9% for power law models and 76.63±2.38% for linear models). The method also generated sufficient accuracy in estimating inhalation parameters such as peak inspiratory flow rate and inspiratory capacity within the presence of noise. Estimating inhaler inhalation flow profiles using audio based methods may be clinically beneficial for inhaler technique training and the remote monitoring of patient adherence. [ABSTRACT FROM AUTHOR]

The rate of lexical replacement estimates the diachronic stability of word forms on the basis of how frequently a proto-language word is replaced or retained in its daughter languages. Lexical replacement rate has been shown to be highly related to word class and word frequency. In this paper, we argue that content words and function words behave differently with respect to lexical replacement rate, and we show that semantic factors predict the lexical replacement rate of content words. For the 167 content items in the Swadesh list, data was gathered on the features of lexical replacement rate, word class, frequency, age of acquisition, synonyms, arousal, imageability and average mutual information, either from published databases or gathered from corpora and lexica. A linear regression model shows that, in addition to frequency, synonyms, senses and imageability are significantly related to the lexical replacement rate of content words–in particular the number of synonyms that a word has. The model shows no differences in lexical replacement rate between word classes, and outperforms a model with word class and word frequency predictors only. [ABSTRACT FROM AUTHOR]