Mixture models of nucleotide sequence evolution that account for heterogeneity in the substitution process across sites and across lineages

Molecular phylogenetic studies of homologous sequences of nucleotides often assume that the underlying evolutionary process was globally stationary, reversible, and homogeneous (SRH), and that a model of evolution with one or more site-specific and time-reversible rate matrices (e.g., the GTR rate matrix) is enough to accurately model the evolution of data over the whole tree. However, an increasing body of data suggests that evolution under these conditions is an exception, rather than the norm. To address this issue, several non-SRH models of molecular evolution have been proposed, but they either ignore heterogeneity in the substitution process across sites (HAS) or assume it can be modeled accurately using the distribution. As an alternative to these models of evolution, we introduce a family of mixture models that approximate HAS without the assumption of an underlying predefined statistical distribution. This family of mixture models is combined with non-SRH models of evolution that account for heterogeneity in the substitution process across lineages (HAL). We also present two algorithms for searching model space and identifying an optimal model of evolution that is less likely to over- or underparameterize the data. The performance of the two new algorithms was evaluated using alignments of nucleotides with 10 000 sites simulated under complex non-SRH conditions on a 25-tipped tree. The algorithms were found to be very successful, identifying the correct HAL model with a 75% success rate (the average success rate for assigning rate matrices to the tree's 48 edges was 99.25%) and, for the correct HAL model, identifying the correct HAS model with a 98% success rate. Finally, parameter estimates obtained under the correct HAL-HAS model were found to be accurate and precise. The merits of our new algorithms were illustrated with an analysis of 42 337 second codon sites extracted from a concatenation of 106 alignments of orthologous genes encoded by the nuclear genomes of Saccharomyces cerevisiae, S. paradoxus, S. mikatae, S. kudriavzevii, S. castellii, S. kluyveri, S. bayanus, and Candida albicans. Our results show that second codon sites in the ancestral genome of these species contained 49.1% invariable sites, 39.6% variable sites belonging to one rate category (V1), and 11.3% variable sites belonging to a second rate category (V2). The ancestral nucleotide content was found to differ markedly across these three sets of sites, and the evolutionary processes operating at the variable sites were found to be non-SRH and best modeled by a combination of eight edge-specific rate matrices (four for V1 and four for V2). The number of substitutions per site at the variable sites also differed markedly, with sites belonging to V1 evolving slower than those belonging to V2 along the lineages separating the seven species of Saccharomyces. Finally, sites belonging to V1 appeared to have ceased evolving along the lineages separating S. cerevisiae, S. paradoxus, S. mikatae, S. kudriavzevii, and S. bayanus, implying that they might have become so selectively constrained that they could be considered invariable sites in these species.

The root locus method: application to linear stability analysis and design of cooperative car-following models

A global framework for linear stability analyses of traffic models, based on the dispersion relation root locus method, is presented and is applied taking the example of a broad class of car-following (CF) models. This approach is able to analyse all aspects of the dynamics: long waves and short wave behaviours, phase velocities and stability features. The methodology is applied to investigate the potential benefits of connected vehicles, i.e. V2V communication enabling a vehicle to send and receive information to and from surrounding vehicles. We choose to focus on the design of the coefficients of cooperation which weights the information from downstream vehicles. The coefficients tuning is performed and different ways of implementing an efficient cooperative strategy are discussed. Hence, this paper brings design methods in order to obtain robust stability of traffic models, with application on cooperative CF models

Predictive models for water sources with high susceptibility for bromine-containing disinfection by-product formation: Implications for water treatment

This study examines a matrix of synthetic water samples designed to include conditions that favour brominated disinfection by-product (Br-DBP) formation, in order to provide predictive models suitable for high Br-DBP forming waters such as salinity-impacted waters. Br-DBPs are known to be more toxic than their chlorinated analogues, in general, and their formation may be favoured by routine water treatment practices such as coagulation/flocculation under specific conditions; therefore, circumstances surrounding their formation must be understood. The chosen factors were bromide concentration, mineral alkalinity, bromide to dissolved organic carbon (Br/DOC) ratio and Suwannee River natural organic matter concentration. The relationships between these parameters and DBP formation were evaluated by response surface modelling of data generated using a face-centred central composite experimental design. Predictive models for ten brominated and/or chlorinated DBPs are presented, as well as models for total trihalomethanes (tTHMs) and total dihaloacetonitriles (tDHANs), and bromide substitution factors for the THMs and DHANs classes. The relationships described revealed that increasing alkalinity and increasing Br/DOC ratio were associated with increasing bromination of THMs and DHANs, suggesting that DOC lowering treatment methods that do not also remove bromide such as enhanced coagulation may create optimal conditions for Br-DBP formation in waters in which bromide is present.

Estimates on the coverage of parameter space using populations of models

In this paper we provide estimates for the coverage of parameter space when using Latin Hypercube Sampling, which forms the basis of building so-called populations of models. The estimates are obtained using combinatorial counting arguments to determine how many trials, k, are needed in order to obtain specified parameter space coverage for a given value of the discretisation size n. In the case of two dimensions, we show that if the ratio (Ø) of trials to discretisation size is greater than 1, then as n becomes moderately large the fractional coverage behaves as 1-exp-ø. We compare these estimates with simulation results obtained from an implementation of Latin Hypercube Sampling using MATLAB.

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study

Background Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival. Methods Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20–84 years diagnosed during 1997–2007 from Queensland, Australia. Results Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients. Conclusions With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings

Optimal management of the critically Ill: Anaesthesia, monitoring, data capture, and point-of-care technological practices in ovine models of critical care

Animal models of critical illness are vital in biomedical research. They provide possibilities for the investigation of pathophysiological processes that may not otherwise be possible in humans. In order to be clinically applicable, the model should simulate the critical care situation realistically, including anaesthesia, monitoring, sampling, utilising appropriate personnel skill mix, and therapeutic interventions. There are limited data documenting the constitution of ideal technologically advanced large animal critical care practices and all the processes of the animal model. In this paper, we describe the procedure of animal preparation, anaesthesia induction and maintenance, physiologic monitoring, data capture, point-of-care technology, and animal aftercare that has been successfully used to study several novel ovine models of critical illness. The relevant investigations are on respiratory failure due to smoke inhalation, transfusion related acute lung injury, endotoxin-induced proteogenomic alterations, haemorrhagic shock, septic shock, brain death, cerebral microcirculation, and artificial heart studies. We have demonstrated the functionality of monitoring practices during anaesthesia required to provide a platform for undertaking systematic investigations in complex ovine models of critical illness.

Spatially explicit structural equation modeling

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated dependent and independent variables. SEM is commonly applied in ecology, but the spatial information commonly found in ecological data remains difficult to model in a SEM framework. Here we propose a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance/covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale and can be implemented using any standard SEM software package. We demonstrate the application of this method using three studies examining the relationships between environmental factors, plant community structure, nitrogen fixation, and plant competition. By design, these data sets had a spatial component, but were previously analyzed using standard SEM models. Using these data sets, we demonstrate the application of SE-SEM to regularly spaced, irregularly spaced, and ad hoc spatial sampling designs and discuss the increased inferential capability of this approach compared with standard SEM. We provide an R package, sesem, to easily implement spatial structural equation modeling.

Burnout and work engagement in occupational therapists

Introduction Work engagement, characterized by vigour, dedication, and absorption, is often perceived as the opposite of burnout. Occupational therapists with burnout feel exhausted and disengaged from their work. This study aims to investigate demographic and work-related psychosocial factors associated with burnout and work engagement. Method A cross-sectional postal survey of 951 occupational therapists was conducted. Findings Two models representing factors associated with burnout (F(15,871) = 28.01, p < .001) and work engagement (F(10,852) = 16.15, p < .001) accounted for 32.54% and 15.93% of the variance respectively. Burnout and work engagement were inversely associated (χ2(n = 941) = 55.16, p < .001). Conclusion Factors associated with burnout and work engagement were identified. The variables associated with burnout included: low psychological detachment from work during out-of-work hours, low income satisfaction, perceived work overload, difficulty saying ‘no’, < 10 years' experience, low frequency of having a ‘belly laugh’, and not having children. High levels of work engagement were reported by therapists with the following: low psychological detachment from work, high income satisfaction, postgraduate qualifications, > 40 hours work/week, high frequency of having a ‘belly laugh’, and having children. Understanding the factors associated with burnout and work engagement provides prerequisite information to inform strategies aimed at building healthy workforces.

Modeling meat yield based on measurements of body traits in genetically improved giant freshwater prawn (GFP) Macrobrachium rosenbergii

A single-generation dataset consisting of 1,730 records from a selection program for high growth rate in giant freshwater prawn (GFP, Macrobrachium rosenbergii) was used to derive prediction equations for meat weight and meat yield. Models were based on body traits [body weight, total length and abdominal width (AW)] and carcass measurements (tail weight and exoskeleton-off weight). Lengths and width were adjusted for the systematic effects of selection line, male morphotypes and female reproductive status, and for the covariables of age at slaughter within sex and body weight. Body and meat weights adjusted for the same effects (except body weight) were used to calculate meat yield (expressed as percentage of tail weight/body weight and exoskeleton-off weight/body weight). The edible meat weight and yield in this GFP population ranged from 12 to 15 g and 37 to 45 %, respectively. The simple (Pearson) correlation coefficients between body traits (body weight, total length and AW) and meat weight were moderate to very high and positive (0.75–0.94), but the correlations between body traits and meat yield were negative (−0.47 to −0.74). There were strong linear positive relationships between measurements of body traits and meat weight, whereas relationships of body traits with meat yield were moderate and negative. Step-wise multiple regression analysis showed that the best model to predict meat weight included all body traits, with a coefficient of determination (R 2) of 0.99 and a correlation between observed and predicted values of meat weight of 0.99. The corresponding figures for meat yield were 0.91 and 0.95, respectively. Body weight or length was the best predictor of meat weight, explaining 91–94 % of observed variance when it was fitted alone in the model. By contrast, tail width explained a lower proportion (69–82 %) of total variance in the single trait models. It is concluded that in practical breeding programs, improvement of meat weight can be easily made through indirect selection for body trait combinations. The improvement of meat yield, albeit being more difficult, is possible by genetic means, with 91 % of the variation in the trait explained by the body and carcass traits examined in this study.

Linear and weakly nonlinear stability analyses of cooperative car-following models

Stability analyses have been widely used to better understand the mechanism of traffic jam formation. In this paper, we consider the impact of cooperative systems (a.k.a. connected vehicles) on traffic dynamics and, more precisely, on flow stability. Cooperative systems are emerging technologies enabling communication between vehicles and/or with the infrastructure. In a distributed communication framework, equipped vehicles are able to send and receive information to/from other equipped vehicles. Here, the effects of cooperative traffic are modeled through a general bilateral multianticipative car-following law that improves cooperative drivers' perception of their surrounding traffic conditions within a given communication range. Linear stability analyses are performed for a broad class of car-following models. They point out different stability conditions in both multianticipative and nonmultianticipative situations. To better understand what happens in unstable conditions, information on the shock wave structure is studied in the weakly nonlinear regime by the mean of the reductive perturbation method. The shock wave equation is obtained for generic car-following models by deriving the Korteweg de Vries equations. We then derive traffic-state-dependent conditions for the sign of the solitary wave (soliton) amplitude. This analytical result is verified through simulations. Simulation results confirm the validity of the speed estimate. The variation of the soliton amplitude as a function of the communication range is provided. The performed linear and weakly nonlinear analyses help justify the potential benefits of vehicle-integrated communication systems and provide new insights supporting the future implementation of cooperative systems.

Postnatal depressive symptoms amongst women in Central Vietnam: A cross-sectional study investigating prevalence and associations with social, cultural and infant factors

Background This study investigated the prevalence and socio-cultural correlates of postnatal mood disturbance amongst women 18–45 years old in Central Vietnam. Son preference and traditional confinement practices were explored as well as factors such as poverty, parity, family and intimate partner relationships and infant health. Methods A cross-sectional study was conducted in twelve randomly selected Commune Health Centres from urban and rural districts of Thua Thien Hue Province, Vietnam. Mother-infant dyads one to six months postpartum were invited to participate. Questionnaires from 431 mothers (urban n = 216; rural n = 215) assessed demographic and family characteristics, traditional confinement practices, son preference, infant health and social capital. The Edinburgh Postnatal Depression Scale (EPDS) and WHO5 Wellbeing Index indicated depressive symptoms and emotional wellbeing. Data were analysed using general linear models. Results Using an EPDS cut-off of 12/13, 18.1 % (n = 78, 95 % CI 14.6 - 22.1) of women had depressive symptoms (20.4 % urban; 15.8 % rural). Contrary to predictions, infant gender and traditional confinement were unrelated to depressive symptoms. Poverty, food insecurity, being frightened of family members, and intimate partner violence increased both depressive symptoms and lowered wellbeing. The first model accounted for 30.2 % of the variance in EPDS score and found being frightened of one’s husband, husband’s unemployment, breastfeeding difficulties, infant diarrhoea, and cognitive social capital were associated with higher EPDS scores. The second model had accounted for 22 % of the variance in WHO5 score. Living in Hue city, low education, poor maternal competence and a negative family response to the baby lowered maternal wellbeing. Conclusions Traditional confinement practices and son preference were not linked to depressive symptoms among mothers, but were correlates of family relationships and wellbeing. Poverty, food insecurity, violence, infant ill health, and discordant intimate and family relationships were linked with depressive symptoms in Central Vietnam.

Social influences on risky driving behaviours among young drivers in Oman

Young drivers represent approximately 20% of the Omani population, yet account for over one third of crash injuries and fatalities on Oman's roads. Internationally, research has demonstrated that social influences play an important role within young driver safety, however, there is little research examining this within Arab gulf countries. This study sought to explore young driver behaviour using Akers' social learning theory. A self-report survey was conducted by 1319 (72.9% male and 27.1% female) young drivers aged 17-25 years. A hierarchical regression model was used to investigate the contribution of social learning variables (norms and behaviour of significant others, personal attitudes towards risky behaviour, imitation of significant others, beliefs about the rewards and punishments offered by risky behaviour), socio-demographic characteristics (age and gender), driving experience (initial training, time driving and previous driving without supervision) and sensitivity to rewards and punishments upon the self-reported risky driving behaviours of young drivers. It was found that 39.6% of the young drivers reported that they have been involved in at least one crash since the issuance of their driving licence and they were considered ‘at fault’ in 60.7% of these crashes. The hierarchical multiple regression models revealed that socio-demographic characteristics and driving experience alone explained 14.2% of the variance in risky driving behaviour. By introducing social learning factors into the model a further 37.0% of variance was explained. Finally, 7.9% of the variance in risky behaviour could be explained by including individual sensitivity to rewards and punishments. These findings and the implications are discussed.

Convergence of regenerative medicine and synthetic biology to develop standardized and validated models of human diseases with clinical relevance

In order to progress beyond currently available medical devices and implants, the concept of tissue engineering has moved into the centre of biomedical research worldwide. The aim of this approach is not to replace damaged tissue with an implant or device but rather to prompt the patient's own tissue to enact a regenerative response by using a tissue-engineered construct to assemble new functional and healthy tissue. More recently, it has been suggested that the combination of Synthetic Biology and translational tissue-engineering techniques could enhance the field of personalized medicine, not only from a regenerative medicine perspective, but also to provide frontier technologies for building and transforming the research landscape in the field of in vitro and in vivo disease models.

Estimating the parameters of stochastic volatility models using option price data

This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity prices. The model is fitted to data on the S&P 500 Index and the prices of vanilla options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.