The time taken for the regional distribution of ventilation to stabilise: An investigation using electrical impedance tomography

Electrical impedance tomography is a novel technology capable of quantifying ventilation distribution in the lung in real time during various therapeutic manoeuvres. The technique requires changes to the patient’s position to place the electrical impedance tomography electrodes circumferentially around the thorax. The impact of these position changes on the time taken to stabilise the regional distribution of ventilation determined by electrical impedance tomography is unknown. This study aimed to determine the time taken for the regional distribution of ventilation determined by electrical impedance tomography to stabilise after changing position. Eight healthy, male volunteers were connected to electrical impedance tomography and a pneumotachometer. After 30 minutes stabilisation supine, participants were moved into 60 degrees Fowler’s position and then returned to supine. Thirty minutes was spent in each position. Concurrent readings of ventilation distribution and tidal volumes were taken every five minutes. A mixed regression model with a random intercept was used to compare the positions and changes over time. The anterior-posterior distribution stabilised after ten minutes in Fowler’s position and ten minutes after returning to supine. Left-right stabilisation was achieved after 15 minutes in Fowler’s position and supine. A minimum of 15 minutes of stabilisation should be allowed for spontaneously breathing individuals when assessing ventilation distribution. This time allows stabilisation to occur in the anterior-posterior direction as well as the left-right direction.

Genome-wide association study for radiographic vertebral fractures: A potential role for the 16q24 BMD locus

Vertebral fracture risk is a heritable complex trait. The aim of this study was to identify genetic susceptibility factors for osteoporotic vertebral fractures applying a genome-wide association study (GWAS) approach. The GWAS discovery was based on the Rotterdam Study, a population-based study of elderly Dutch individuals aged >55years; and comprising 329 cases and 2666 controls with radiographic scoring (McCloskey-Kanis) and genetic data. Replication of one top-associated SNP was pursued by de-novo genotyping of 15 independent studies across Europe, the United States, and Australia and one Asian study. Radiographic vertebral fracture assessment was performed using McCloskey-Kanis or Genant semi-quantitative definitions. SNPs were analyzed in relation to vertebral fracture using logistic regression models corrected for age and sex. Fixed effects inverse variance and Han-Eskin alternative random effects meta-analyses were applied. Genome-wide significance was set at p<5×10-8. In the discovery, a SNP (rs11645938) on chromosome 16q24 was associated with the risk for vertebral fractures at p=4.6×10-8. However, the association was not significant across 5720 cases and 21,791 controls from 14 studies. Fixed-effects meta-analysis summary estimate was 1.06 (95% CI: 0.98-1.14; p=0.17), displaying high degree of heterogeneity (I2=57%; Qhet p=0.0006). Under Han-Eskin alternative random effects model the summary effect was significant (p=0.0005). The SNP maps to a region previously found associated with lumbar spine bone mineral density (LS-BMD) in two large meta-analyses from the GEFOS consortium. A false positive association in the GWAS discovery cannot be excluded, yet, the low-powered setting of the discovery and replication settings (appropriate to identify risk effect size >1.25) may still be consistent with an effect size <1.10, more of the type expected in complex traits. Larger effort in studies with standardized phenotype definitions is needed to confirm or reject the involvement of this locus on the risk for vertebral fractures.

Determinants of the citation rate of medical research publications from a developing country

Background The number of citations received by an article is considered as an objective marker judging the importance and the quality of the research work. The present study aims to study the determinants of citations for research articles published by Sri Lankan authors. Methods Papers were selectively retrieved from the SciVerse Scopus® (Elsevier Properties S.A, USA) database for 10 years from 1st January 1997 to 31st December 2006, of which 50% were selected for inclusion by simple random sampling. The primary outcome measure was citation rate (defined as the number of citations during the 2 subsequent years after publication). Citation data was collected using the SciVerse Scopus® Citation Analyzer and self citations were excluded. A linear regression analysis was performed with ‘number of citations’ as the continuous dependent variable and other independent variables. Result The number of publications has steadily increased during the period of study. Over three quarter of papers were published in international journals. More than half of publications were research studies (55.3%), and most of the research studies were descriptive cross-sectional studies (27.1%). The mean number of citations within 2 years of publication was 1.7 and 52.1% of papers were not cited within the first two years of publication. The mean number of citations for collaborative studies (2.74) was significantly higher than that of non-collaborative studies (0.66). The mean number of citations did not significantly change depending on whether the publication had a positive result (2.08) or not (2.92) and was also not influenced by the presence (2.30) or absence (1.99) of the main study conclusion in the title of the article. In the linear regression model, the journal rank, number of authors, conducting the study abroad, being a research study or systematic review/meta-analysis and having regional and/or international collaboration all significantly increased the number of citations. Conclusion The journal rank, number of authors, conducting the study abroad, being a research study or systematic review/meta-analysis and having regional and/or international collaboration all significantly increased the number of citations. However, the presence of a positive result in the study did not influence the citation rate.

Rank regression for analyzing ordinal qualitative data for treatment comparison

Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the underlying assumptions such as normality and homogeneous variances are often violated for qualitative data. To this end, we investigated an alternative methodology, rank regression, for analyzing the ordinal data. The rank-based methods are essentially based on pairwise comparisons and, therefore, can deal with qualitative data naturally. They require neither normality assumption nor data transformation. Apart from robustness against outliers and high efficiency, the rank regression can also incorporate covariate effects in the same way as the ordinary regression. By reanalyzing a data set from a wheat Fusarium crown rot study, we illustrated the use of the rank regression methodology and demonstrated that the rank regression models appear to be more appropriate and sensible for analyzing nonnormal data and data with outliers.

Spatial model of site index based on Y-ray spectrometry and a digital elevation model for two Pinus species in Tuan Toolara State Forest, Queensland, Australia

Site index prediction models are an important aid for forest management and planning activities. This paper introduces a multiple regression model for spatially mapping and comparing site indices for two Pinus species (Pinus elliottii Engelm. and Queensland hybrid, a P. elliottii x Pinus caribaea Morelet hybrid) based on independent variables derived from two major sources: g-ray spectrometry (potassium (K), thorium (Th), and uranium (U)) and a digital elevation model (elevation, slope, curvature, hillshade, flow accumulation, and distance to streams). In addition, interpolated rainfall was tested. Species were coded as a dichotomous dummy variable; interaction effects between species and the g-ray spectrometric and geomorphologic variables were considered. The model explained up to 60% of the variance of site index and the standard error of estimate was 1.9 m. Uranium, elevation, distance to streams, thorium, and flow accumulation significantly correlate to the spatial variation of the site index of both species, and hillshade, curvature, elevation and slope accounted for the extra variability of one species over the other. The predicted site indices varied between 20.0 and 27.3 m for P. elliottii, and between 23.1 and 33.1 m for Queensland hybrid; the advantage of Queensland hybrid over P. elliottii ranged from 1.8 to 6.8 m, with the mean at 4.0 m. This compartment-based prediction and comparison study provides not only an overview of forest productivity of the whole plantation area studied but also a management tool at compartment scale.

Calculating the Fickian diffusivity for a lattice-based random walk with agents and obstacles of different shapes and sizes

Random walk models are often used to interpret experimental observations of the motion of biological cells and molecules. A key aim in applying a random walk model to mimic an in vitro experiment is to estimate the Fickian diffusivity (or Fickian diffusion coefficient),D. However, many in vivo experiments are complicated by the fact that the motion of cells and molecules is hindered by the presence of obstacles. Crowded transport processes have been modeled using repeated stochastic simulations in which a motile agent undergoes a random walk on a lattice that is populated by immobile obstacles. Early studies considered the most straightforward case in which the motile agent and the obstacles are the same size. More recent studies considered stochastic random walk simulations describing the motion of an agent through an environment populated by obstacles of different shapes and sizes. Here, we build on previous simulation studies by analyzing a general class of lattice-based random walk models with agents and obstacles of various shapes and sizes. Our analysis provides exact calculations of the Fickian diffusivity, allowing us to draw conclusions about the role of the size, shape and density of the obstacles, as well as examining the role of the size and shape of the motile agent. Since our analysis is exact, we calculateDdirectly without the need for random walk simulations. In summary, we find that the shape, size and density of obstacles has a major influence on the exact Fickian diffusivity. Furthermore, our results indicate that the difference in diffusivity for symmetric and asymmetric obstacles is significant.

Joint Regression and Association Models for Repeated Categorical Responses

The focus of this study is on statistical analysis of categorical responses, where the response values are dependent of each other. The most typical example of this kind of dependence is when repeated responses have been obtained from the same study unit. For example, in Paper I, the response of interest is the pneumococcal nasopharengyal carriage (yes/no) on 329 children. For each child, the carriage is measured nine times during the first 18 months of life, and thus repeated respones on each child cannot be assumed independent of each other. In the case of the above example, the interest typically lies in the carriage prevalence, and whether different risk factors affect the prevalence. Regression analysis is the established method for studying the effects of risk factors. In order to make correct inferences from the regression model, the associations between repeated responses need to be taken into account. The analysis of repeated categorical responses typically focus on regression modelling. However, further insights can also be gained by investigating the structure of the association. The central theme in this study is on the development of joint regression and association models. The analysis of repeated, or otherwise clustered, categorical responses is computationally difficult. Likelihood-based inference is often feasible only when the number of repeated responses for each study unit is small. In Paper IV, an algorithm is presented, which substantially facilitates maximum likelihood fitting, especially when the number of repeated responses increase. In addition, a notable result arising from this work is the freely available software for likelihood-based estimation of clustered categorical responses.

A statistical analysis of audience sound absorption (A)

The absorption produced by the audience in concert halls is considered a random variable. Beranek's proposal [L. L. Beranek, Music, Acoustics and Architecture (Wiley, New York, 1962), p. 543] that audience absorption is proportional to the area they occupy and not to their number is subjected to a statistical hypothesis test. A two variable linear regression model of the absorption with audience area and residual area as regressor variables is postulated for concert halls without added absorptive materials. Since Beranek's contention amounts to the statement that audience absorption is independent of the seating density, the test of the hypothesis lies in categorizing halls by seating density and examining for significant differences among slopes of regression planes of the different categories. Such a test shows that Beranek's hypothesis can be accepted. It is also shown that the audience area is a better predictor of the absorption than the audience number. The absorption coefficients and their 95% confidence limits are given for the audience and residual areas. A critique of the regression model is presented.

Optimization of an ammonia reactor using regression analysis

This paper presents an optimization algorithm for an ammonia reactor based on a regression model relating the yield to several parameters, control inputs and disturbances. This model is derived from the data generated by hybrid simulation of the steady-state equations describing the reactor behaviour. The simplicity of the optimization program along with its ability to take into account constraints on flow variables make it best suited in supervisory control applications.

Molecular Dynamics Investigation of Structure and Transport in the K2O-2SiO2 System Using a Partial Charge Based Model Potential

Potassium disilicate glass and melt have been investigated by using a new partial charge based potential model in which nonbridging oxygens are differentiated from bridging oxygens by their charges. The model reproduces the structural data pertaining to the coordination polyhedra around potassium and the various bond angle distributions excellently. The dynamics of the glass has been studied by using space and time correlation functions. It is found that K ions migrate by a diffusive mechanism in the melt and by hops below the glass transition temperature. They are also found to migrate largely through nonbridging oxygenrich sites in the silicate matrix, thus providing support to the predictions of the modified random network model.

Molecular dynamics Investigation of structure and transport in the K2O-2SiO2 system using a partial charge based model potential

Potassium disilicate glass and melt have been investigated by using anew partial charge based potential model in which nonbridging oxygens are differentiated from bridging oxygens by their charges. The model reproduces the structural data pertaining to the coordination polyhedra around potassium and the various bond angle distributions excellently. The dynamics of the glass has been studied by using space and time correlation functions. It is found that K ions migrate by a diffusive mechanism in the melt and by hops below the glass transition temperature. They are also found to migrate largely through nonbridging oxygen-rich sites in the silicate matrix, thus providing support to the predictions of the modified random network model.

Large-MIMO Receiver based on Linear Regression of MMSE Residual

Multiple input multiple output (MIMO) systems with large number of antennas have been gaining wide attention as they enable very high throughputs. A major impediment is the complexity at the receiver needed to detect the transmitted data. To this end we propose a new receiver, called LRR (Linear Regression of MMSE Residual), which improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel), to find the linear regression parameters. The proposed receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs quite well: at a bit error rate (BER) of 10(-3), the SNR gain over MMSE receiver is about 7 dB for a 16 x 16 system; for a 64 x 64 system the gain is about 8.5 dB. For large coherence time, the complexity order of the LRR receiver is the same as that of the MMSE receiver, and in simulations we find that it needs about 4 times as many floating point operations. We also show that further gain of about 4 dB is obtained by local search around the estimate given by the LRR receiver.

Optimization of degree of sphericity of primary phase during cooling slope casting of A356 Al alloy: Taguchi method and regression analysis

The present work presents the results of experimental investigation of semi-solid rheocasting of A356 Al alloy using a cooling slope. The experiments have been carried out following Taguchi method of parameter design (orthogonal array of L-9 experiments). Four key process variables (slope angle, pouring temperature, wall temperature, and length of travel of the melt) at three different levels have been considered for the present experimentation. Regression analysis and analysis of variance (ANOVA) has also been performed to develop a mathematical model for degree of sphericity evolution of primary alpha-Al phase and to find the significance and percentage contribution of each process variable towards the final outcome of degree of sphericity, respectively. The best processing condition has been identified for optimum degree of sphericity (0.83) as A(3), B-3, C-2, D-1 i.e., slope angle of 60 degrees, pouring temperature of 650 degrees C, wall temperature 60 degrees C, and 500 mm length of travel of the melt, based on mean response and signal to noise ratio (SNR). ANOVA results shows that the length of travel has maximum impact on degree of sphericity evolution. The predicted sphericity obtained from the developed regression model and the values obtained experimentally are found to be in good agreement with each other. The sphericity values obtained from confirmation experiment, performed at 95% confidence level, ensures that the optimum result is correct and also the confirmation experiment values are within permissible limits. (c) 2014 Elsevier Ltd. All rights reserved.

Um estudo da atenção primária à saúde mediante o indicador: Internações por condições sensíveis à atenção ambulatorial (CSAA)

A Atenção Primária à Saúde - APS é reconhecida como o nível fundamental e porta de entrada do sistema de atenção à saúde, sendo o lugar adequado onde pode ser atendida e resolvida a maior parte dos problemas de saúde. É considerada pela OMS como a principal proposta de modelo assistencial. Essa importância da APS leva a necessidade de pesquisas avaliativas dos seus resultados para adequação e melhoria de políticas e planos de ação delineados em relação à mesma. Pesquisas internacionais e nacionais são realizadas, nas quais indicadores relativos às atividades hospitalares estão sendo empregados com o objetivo de medir resultados como efetividade e acesso da APS. Um desses indicadores, desenvolvido por John Billings da Universidade de Nova York, na década de 90, consiste nas condições pelas quais as internações hospitalares por Condições Sensíveis à Atenção Ambulatorial (CSAA) deveriam ser evitadas caso os serviços da APS fossem efetivos e acessíveis. Utilizando-se o SIH-AIH/2008 e a lista brasileira de Internações por Condições Sensíveis a Atenção Primária, publicada em 2008, a proposta do presente trabalho é a de estudar os cuidados primários à saúde baseando-se nas ICSAA, na área urbana da cidade de Juiz de Fora-MG. Buscou-se responder sobre os efeitos que ocorrem nessas internações a partir das características individuais dos pacientes, das características das Unidades Básicas de Saúde-UBS (infraestrutura, produção e modelos assistenciais) e das condições sócio-econômicas/ambientais das áreas cobertas por UAPS e descobertas (sem UAPS), com a utilização de modelos multiníveis logísticos com intercepto aleatório. Buscou-se conhecer, também, a distribuição espacial das taxas padronizadas por idade das ICSAA nessas áreas e suas associações com as variáveis contextuais, utilizando-se ferramentas da análise espacial. Os resultados do presente trabalho mostraram que a porcentagem de internações por CSAA, foi de 4,1%. Os modelos assistenciais ESF e o Modelo Tradicional, base da organização da atenção primária no Brasil, não apresentaram no município, impacto significativo nas ICSAA, somente na forma de áreas descobertas tendo como referência as áreas cobertas. Também não foram significativas as variáveis de infraestrutura e produção das UAPS. Os efeitos individuais (idade e sexo) nas ICSAA foram significativos, apresentando probabilidades de significância menores que 1%, o mesmo acontecendo com o Índice de Desenvolvimento Social-IDS, que contempla as condições sociais, econômicas e ambientais das áreas analisadas. A distribuição espacial das taxas padronizadas por idade apresentou padrão aleatório e os testes dos Multiplicadores de Lagrange não foram significativos indicando o modelo de regressão clássico (MQO) como adequado para explicar as taxas em função das variáveis contextuais. Para a análise conjunta das áreas cobertas e descobertas foram fatores de risco: a variável econômica (% dos domicílios com renda até 2 SM), áreas descobertas tendo como referência as áreas cobertas e a região nordeste do município. Para as áreas cobertas as variáveis de produção das UAPS, econômica e a região nordeste apresentaram como fator de risco para as taxas de internação por CSAA.

Transtorno do Estresse Pós-Traumático e alterações lipídicas

O transtorno do estresse pós-traumático (TEPT) e alterações lipídicas são as temáticas principais dessa Dissertação. Seu objetivo principal foi investigar a associação entre o TEPT e as concentrações séricas de colesterol total (CT), lipoproteína de baixa densidade (LDL), lipoproteína de alta densidade (HDL) e triglicerídeos (TG) através de uma revisão sistemática da literatura seguida de metanálise. Adicionalmente, a relação entre essas variáveis lipídicas e os grupos de sintomas do TEPT revivescência, esquiva/entorpecimento emocional e hiperestimulação autonômica foi avaliada em um segundo estudo com dados primários. A metanálise incluiu 18 artigos, totalizando 2.110 indivíduos com TEPT e 17.550 indivíduos sem TEPT. As diferenças de médias ponderadas (DMP) mg/dL dos parâmetros lipídicos foram calculadas por modelos de efeitos aleatórios e modelos de meta-regressão foram ajustados para investigar possíveis fontes de heterogeneidade. O estudo encontrou que o TEPT foi associado a um pior perfil lipídico quando comparados a controles sem o transtorno (DMPCT= 20,57, IC 95% 12,21 28,93; DMPLDL= 12,11, IC 95% 5,89 18,32; DMPHDL= -3,73, IC 95% -5,97 -1,49; DMPTG= 35,87, IC 95% 21,12 50,61). A heterogeneidade estatística entre os resultados dos estudos foi alta para todos os parâmetros lipídicos e a variável que mais pareceu explicar essas inconsistências foi idade. O segundo artigo faz parte de um estudo maior conduzido em 2004 com 157 policiais do sexo masculino do Batalhão de Choque da Polícia Militar do Estado de Goiás (BPMCHOQUE). Somente oficiais de férias ou em dispensa inclusive dispensa médica não foram avaliados. O instrumento utilizado para o rastreio do TEPT foi a versão em português para civis da Post-Traumatic Stress Disorder Checklist (PCL-C). Trinta e nove participantes (25%) foram excluídos do estudo: dois porque falharam no preenchimento dos questionários e 37 cujas amostras de sangue não foram coletadas por vários motivos. Neste trabalho, encontrou-se uma forte correlação positiva entre as concentrações séricas de CT e LDL com o grupo de sintomas de hiperestimulação autonômica, somente no grupo TEPT: ρ= 0,89 (p<0,01) e ρ =0,92 (p<0,01), respectivamente. Em suma, espera-se que os resultados dessa Dissertação possam colaborar para o estabelecimento de um melhor acompanhamento clínico de pacientes com TEPT, particularmente porque estes parecem estar sob um maior risco de doenças cardiovasculares devido a um pior perfil lipídico.