Polysemy advantage with abstract but not concrete words

Peer reviewed

Applied evolution: An integrated approach to studying life history traits in response to drug selection

The use of chemical control measures to reduce the impact of parasite and pest species has frequently resulted in the development of resistance. Thus, resistance management has become a key concern in human and veterinary medicine, and in agricultural production. Although it is known that factors such as gene flow between susceptible and resistant populations, drug type, application methods, and costs of resistance can affect the rate of resistance evolution, less is known about the impacts of density-dependent eco-evolutionary processes that could be altered by drug-induced mortality. The overall aim of this thesis was to take an experimental evolution approach to assess how life history traits respond to drug selection, using a free-living dioecious worm (Caenorhabditis remanei) as a model. In Chapter 2, I defined the relationship between C. remanei survival and Ivermectin dose over a range of concentrations, in order to control the intensity of selection used in the selection experiment described in Chapter 4. The dose-response data were also used to appraise curve-fitting methods, using Akaike Information Criterion (AIC) model selection to compare a series of nonlinear models. The type of model fitted to the dose response data had a significant effect on the estimates of LD50 and LD99, suggesting that failure to fit an appropriate model could give misleading estimates of resistance status. In addition, simulated data were used to establish that a potential cost of resistance could be predicted by comparing survival at the upper asymptote of dose-response curves for resistant and susceptible populations, even when differences were as low as 4%. This approach to dose-response modeling ensures that the maximum amount of useful information relating to resistance is gathered in one study. In Chapter 3, I asked how simulations could be used to inform important design choices used in selection experiments. Specifically, I focused on the effects of both within- and between-line variation on estimated power, when detecting small, medium and large effect sizes. Using mixed-effect models on simulated data, I demonstrated that commonly used designs with realistic levels of variation could be underpowered for substantial effect sizes. Thus, use of simulation-based power analysis provides an effective way to avoid under or overpowering a study designs incorporating variation due to random effects. In Chapter 4, I 3 investigated how Ivermectin dosage and changes in population density affect the rate of resistance evolution. I exposed replicate lines of C. remanei to two doses of Ivermectin (high and low) to assess relative survival of lines selected in drug-treated environments compared to untreated controls over 10 generations. Additionally, I maintained lines where mortality was imposed randomly to control for differences in density between drug treatments and to distinguish between the evolutionary consequences of drug treatment versus ecological processes affected by changes in density-dependent feedback. Intriguingly, both drug-selected and random-mortality lines showed an increase in survivorship when challenged with Ivermectin; the magnitude of this increase varied with the intensity of selection and life-history stage. The results suggest that interactions between density-dependent processes and life history may mediate evolved changes in susceptibility to control measures, which could result in misleading conclusions about the evolution of heritable resistance following drug treatment. In Chapter 5, I investigated whether the apparent changes in drug susceptibility found in Chapter 4 were related to evolved changes in life-history of C. remanei populations after selection in drug-treated and random-mortality environments. Rapid passage of lines in the drug-free environment had no effect on the measured life-history traits. In the drug-free environment, adult size and fecundity of drug-selected lines increased compared to the controls but drug selection did not affect lifespan. In the treated environment, drug-selected lines showed increased lifespan and fecundity relative to controls. Adult size of randomly culled lines responded in a similar way to drug-selected lines in the drug-free environment, but no change in fecundity or lifespan was observed in either environment. The results suggest that life histories of nematodes can respond to selection as a result of the application of control measures. Failure to take these responses into account when applying control measures could result in adverse outcomes, such as larger and more fecund parasites, as well as over-estimation of the development of genetically controlled resistance. In conclusion, my thesis shows that there may be a complex relationship between drug selection, density-dependent regulatory processes and life history of populations challenged with control measures. This relationship could have implications for how resistance is monitored and managed if life histories of parasitic species show such eco-evolutionary responses to drug application.

Evaluation and management of demersal stocks in the Adriatic: the case of common sole (Solea solea, L.) in the northern and central Adriatic Sea

This PhD thesis aimed to assess the status of common sole, one of the main commercial stocks in the Adriatic Sea, using a mix of conventional and innovative techniques to provide more reliable estimates of stock status compared to past advice. First, a meta-analysis was carried out using data-poor assessment model to analyze the whole catch assemblage of rapido fishery. The outcomes were used to estimate rebuilding time and forecast catches under different harvest control rule scenarios, with a reduction of 20% of fishing effort being suggested as a way to allow most of the species to recover to sustainable levels. Secondly, an ensemble of data-rich assessment models was developed to better incorporate uncertainty by using alternative hypotheses of main parameters. This was the first time an ensemble of models has been used in the Mediterranean to provide management advice. Consistent with data-poor analysis results, the ensemble outcomes indicated that the common sole stock was showing a recovering trend probably due to the effective management actions underway in the area rather than the moderate effort reduction according to the actual management plan. Moreover, back-calculation measurements were used to fit and compare monophasic and biphasic growth curves through the use of non-linear mixed effects models. The analyses revealed that the fitting of the biphasic curve was superior, confirming the theory that growth in size would decrease as a consequence of reproductive effort. A stock assessment simulation showed how the use of the monophasic pattern would result in a critical overestimation of biomass that could lead to a greater risk of overfishing. As a final step, a simulation-testing procedure was applied to determine the best performing reference points using stock-specific characteristic. The procedure could be routinely adopted to increase transparency in reference points calculation enhancing the credibility of scientific advice.

A Poisson mixed model with nonnormal random effect distribution

In this paper, we propose a random intercept Poisson model in which the random effect is assumed to follow a generalized log-gamma (GLG) distribution. This random effect accommodates (or captures) the overdispersion in the counts and induces within-cluster correlation. We derive the first two moments for the marginal distribution as well as the intraclass correlation. Even though numerical integration methods are, in general, required for deriving the marginal models, we obtain the multivariate negative binomial model from a particular parameter setting of the hierarchical model. An iterative process is derived for obtaining the maximum likelihood estimates for the parameters in the multivariate negative binomial model. Residual analysis is proposed and two applications with real data are given for illustration. (C) 2011 Elsevier B.V. All rights reserved.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

Algebraic structure for interaction on mixed models

Binary operations on commutative Jordan algebras, CJA, can be used to study interactions between sets of factors belonging to a pair of models in which one nests the other. It should be noted that from two CJA we can, through these binary operations, build CJA. So when we nest the treatments from one model in each treatment of another model, we can study the interactions between sets of factors of the first and the second models.

The effect of antiretroviral treatment of different durations in primary HIV infection.

OBJECTIVES: To compare immunological, virological and clinical outcomes in persons initiating combination antiretroviral therapy (cART of different durations within 6 months of seroconversion (early treated) with those who deferred therapy (deferred group). DESIGN: CD4 cell and HIV-RNA measurements for 'early treated' individuals following treatment cessation were compared with the corresponding ART-free period for the 'deferred' group using piecewise linear mixed models. Individuals identified during primary HIV infection were included if they seroconverted from 1st January 1996 and were at least 15 years of age at seroconversion. Those with at least 2 CD4 less than 350 cells/microl or AIDS within the first 6 months following seroconversion were excluded. RESULTS: Of 348 'early treated' patients, 147 stopped cART following treatment for at least 6 (n = 38), more than 6-12 (n = 40) or more than 12 months (n = 69). CD4 cell loss was steeper for the first 6 months following cART cessation, but subsequent loss rate was similar to the 'deferred' group (n = 675, P = 0.26). Although those treated for more than 12 months appeared to maintain higher CD4 cell counts following cART cessation, those treated for 12 months or less had CD4 cell counts 6 months after cessation comparable to those in the 'deferred' group. There was no difference in HIV-RNA set points between the 'early' and 'deferred' groups (P = 0.57). AIDS rates were similar but death rates, mainly due to non-AIDS causes, were higher in the 'deferred' group (P = 0.05). CONCLUSION: Transient cART, initiated within 6 months of seroconversion, seems to have no effect on viral load set point and limited beneficial effect on CD4 cell levels in individuals treated for more than 12 months. Its long-term effects remain inconclusive and need further investigation.

Effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk: a test with plants in an arid environment

Aim To evaluate the effects of using distinct alternative sets of climatic predictor variables on the performance, spatial predictions and future projections of species distribution models (SDMs) for rare plants in an arid environment. . Location Atacama and Peruvian Deserts, South America (18º30'S - 31º30'S, 0 - 3 000 m) Methods We modelled the present and future potential distributions of 13 species of Heliotropium sect. Cochranea, a plant group with a centre of diversity in the Atacama Desert. We developed and applied a sequential procedure, starting from climate monthly variables, to derive six alternative sets of climatic predictor variables. We used them to fit models with eight modelling techniques within an ensemble forecasting framework, and derived climate change projections for each of them. We evaluated the effects of using these alternative sets of predictor variables on performance, spatial predictions and projections of SDMs using Generalised Linear Mixed Models (GLMM). Results The use of distinct sets of climatic predictor variables did not have a significant effect on overall metrics of model performance, but had significant effects on present and future spatial predictions. Main conclusion Using different sets of climatic predictors can yield the same model fits but different spatial predictions of current and future species distributions. This represents a new form of uncertainty in model-based estimates of extinction risk that may need to be better acknowledged and quantified in future SDM studies.

Measuring the relative effect of factors affecting species distribution model predictions

1. Species distribution models are increasingly used to address conservation questions, so their predictive capacity requires careful evaluation. Previous studies have shown how individual factors used in model construction can affect prediction. Although some factors probably have negligible effects compared to others, their relative effects are largely unknown. 2. We introduce a general "virtual ecologist" framework to study the relative importance of factors involved in the construction of species distribution models. 3. We illustrate the framework by examining the relative importance of five key factors-a missing covariate, spatial autocorrelation due to a dispersal process in presences/absences, sample size, sampling design and modeling technique-in a real study framework based on plants in a mountain landscape at regional scale, and show that, for the parameter values considered here, most of the variation in prediction accuracy is due to sample size and modeling technique. Contrary to repeatedly reported concerns, spatial autocorrelation has only comparatively small effects. 4. This study shows the importance of using a nested statistical framework to evaluate the relative effects of factors that may affect species distribution models.

Defining the Effect of the 16p11.2 Duplication on Cognition, Behavior, and Medical Comorbidities.

IMPORTANCE: The 16p11.2 BP4-BP5 duplication is the copy number variant most frequently associated with autism spectrum disorder (ASD), schizophrenia, and comorbidities such as decreased body mass index (BMI). OBJECTIVES: To characterize the effects of the 16p11.2 duplication on cognitive, behavioral, medical, and anthropometric traits and to understand the specificity of these effects by systematically comparing results in duplication carriers and reciprocal deletion carriers, who are also at risk for ASD. DESIGN, SETTING, AND PARTICIPANTS: This international cohort study of 1006 study participants compared 270 duplication carriers with their 102 intrafamilial control individuals, 390 reciprocal deletion carriers, and 244 deletion controls from European and North American cohorts. Data were collected from August 1, 2010, to May 31, 2015 and analyzed from January 1 to August 14, 2015. Linear mixed models were used to estimate the effect of the duplication and deletion on clinical traits by comparison with noncarrier relatives. MAIN OUTCOMES AND MEASURES: Findings on the Full-Scale IQ (FSIQ), Nonverbal IQ, and Verbal IQ; the presence of ASD or other DSM-IV diagnoses; BMI; head circumference; and medical data. RESULTS: Among the 1006 study participants, the duplication was associated with a mean FSIQ score that was lower by 26.3 points between proband carriers and noncarrier relatives and a lower mean FSIQ score (16.2-11.4 points) in nonproband carriers. The mean overall effect of the deletion was similar (-22.1 points; P < .001). However, broad variation in FSIQ was found, with a 19.4- and 2.0-fold increase in the proportion of FSIQ scores that were very low (≤40) and higher than the mean (>100) compared with the deletion group (P < .001). Parental FSIQ predicted part of this variation (approximately 36.0% in hereditary probands). Although the frequency of ASD was similar in deletion and duplication proband carriers (16.0% and 20.0%, respectively), the FSIQ was significantly lower (by 26.3 points) in the duplication probands with ASD. There also were lower head circumference and BMI measurements among duplication carriers, which is consistent with the findings of previous studies. CONCLUSIONS AND RELEVANCE: The mean effect of the duplication on cognition is similar to that of the reciprocal deletion, but the variance in the duplication is significantly higher, with severe and mild subgroups not observed with the deletion. These results suggest that additional genetic and familial factors contribute to this variability. Additional studies will be necessary to characterize the predictors of cognitive deficits.

Sampling, WLS, and Mixed Models

Mixed models may be defined with or without reference to sampling, and can be used to predict realized random effects, as when estimating the latent values of study subjects measured with response error. When the model is specified without reference to sampling, a simple mixed model includes two random variables, one stemming from an exchangeable distribution of latent values of study subjects and the other, from the study subjects` response error distributions. Positive probabilities are assigned to both potentially realizable responses and artificial responses that are not potentially realizable, resulting in artificial latent values. In contrast, finite population mixed models represent the two-stage process of sampling subjects and measuring their responses, where positive probabilities are only assigned to potentially realizable responses. A comparison of the estimators over the same potentially realizable responses indicates that the optimal linear mixed model estimator (the usual best linear unbiased predictor, BLUP) is often (but not always) more accurate than the comparable finite population mixed model estimator (the FPMM BLUP). We examine a simple example and provide the basis for a broader discussion of the role of conditioning, sampling, and model assumptions in developing inference.

Hglm: A package for fitting hierarchical generalized linear models

We present the hglm package for fitting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

Genetic heterogeneity of residual variance : estimation of variance components using double hierarchical generalized linear models

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms. Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model. Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

LIKELIHOOD ESTIMATION OF CONJUGACY RELATIONSHIPS IN LINEAR MODELS WITH APPLICATIONS TO HIGH-THROUGHPUT GENOMICS

In the simultaneous estimation of a large number of related quantities, multilevel models provide a formal mechanism for efficiently making use of the ensemble of information for deriving individual estimates. In this article we investigate the ability of the likelihood to identify the relationship between signal and noise in multilevel linear mixed models. Specifically, we consider the ability of the likelihood to diagnose conjugacy or independence between the signals and noises. Our work was motivated by the analysis of data from high-throughput experiments in genomics. The proposed model leads to a more flexible family. However, we further demonstrate that adequately capitalizing on the benefits of a well fitting fully-specified likelihood in the terms of gene ranking is difficult.

Effect of routine claw trimming on claw temperature in dairy cows measured by infrared thermography

Infrared thermography (IRT) was used to assess the effect of routine claw trimming on claw temperature. In total, 648 IRT observations each were collected from 81 cows housed in 6 tiestalls before and 3 wk after claw trimming. The feet were classified as either healthy (nonlesion group, n = 182) or affected with infectious foot disorders (group IFD, n = 142). The maximal surface temperatures of the coronary band and skin and the difference of the maximal temperatures (ΔT) between the lateral and medial claws of the respective foot were assessed. Linear mixed models, correcting for the hierarchical structure of the data, ambient temperature, and infectious status of the claws, were developed to evaluate the effect of time in relation to the trimming event (d 0 versus d 21) and claw (medial versus lateral). Front feet and hind feet were analyzed separately. Ambient temperature and infectious foot status were identified as external and internal factors, respectively, that significantly affected claw temperature. Before claw trimming, the lateral claws of the hind feet were significantly warmer compared with the medial claws, whereas such a difference was not evident for the claws of the front feet. At d 21, ΔT of the hind feet was reduced by ≥ 0.25 °C, whereas it was increased by ≤ 0.13 °C in the front feet compared with d 0. Therefore, trimming was associated with a remarkable decrease of ΔT of the hind claws. Equalizing the weight bearing of the hind feet by routine claw trimming is associated with a measurable reduction of ΔT between the paired hind claws.