Inference of Gene Flow in the Process of Speciation: An Efficient Maximum-Likelihood Method for the Isolation-with-Initial-Migration Model.

The isolation-with-migration (IM) model is commonly used to make inferences about gene flow during speciation, using polymorphism data. However, Becquet and Przeworski (2009) report that the parameter estimates obtained by fitting the IM model are very sensitive to the model's assumptions, including the assumption of constant gene flow until the present. This paper is concerned with the isolation-with-initial-migration (IIM) model, which drops precisely this assumption. In the IIM model, one ancestral population divides into two descendant subpopulations, between which there is an initial period of gene flow and a subsequent period of isolation. We derive a very fast method of fitting an extended version of the IIM model, which also allows for asymmetric gene flow and unequal population sizes. This is a maximum-likelihood method, applicable to data on the number of segregating sites between pairs of DNA sequences from a large number of independent loci. In addition to obtaining parameter estimates, our method can also be used, by means of likelihood ratio tests, to distinguish between alternative models representing the following divergence scenarios: a) divergence with potentially asymmetric gene flow until the present; b) divergence with potentially asymmetric gene flow until some point in the past and in isolation since then; c) divergence in complete isolation. We illustrate the procedure on pairs of Drosophila sequences from approximately 30,000 loci. The computing time needed to fit the most complex version of the model to this data set is only a couple of minutes. The R code to fit the IIM model can be found in the supplementary files of this paper

Inference of gene flow in the process of speciation: Efficient maximum-likelihood implementation of a generalised isolation-with-migration model

The 'isolation with migration' (IM) model has been extensively used in the literature to detect gene flow during the process of speciation. In this model, an ancestral population split into two or more descendant populations which subsequently exchanged migrants at a constant rate until the present. Of course, the assumption of constant gene flow until the present is often over-simplistic in the context of speciation. In this paper, we consider a 'generalised IM' (GIM) model: a two-population IM model in which migration rates and population sizes are allowed to change at some point in the past. By developing a maximum-likelihood implementation of this model, we enable inference on both historical and contemporary rates of gene flow between two closely related populations or species. The GIM model encompasses both the standard two-population IM model and the 'isolation with initial migration' (IIM) model as special cases, as well as a model of secondary contact. We examine for simulated data how our method can be used, by means of likelihood ratio tests or AIC scores, to distinguish between the following scenarios of population divergence: (a) divergence in complete isolation; (b) divergence with a period of gene flow followed by isolation; (c) divergence with a period of isolation followed by secondary contact; (d) divergence with ongoing gene flow. Our method is based on the coalescent and is suitable for data sets consisting of the number of nucleotide differences between one pair of DNA sequences at each of a large number of independent loci. As our method relies on an explicit expression for the likelihood, it is computationally very fast

The comprehensive cohort model in a pilot trial in orthopaedic trauma

Background: The primary aim of this study was to provide an estimate of effect size for the functional outcome of operative versus non-operative treatment for patients with an acute rupture of the Achilles tendon using accelerated rehabilitation for both groups of patients. The secondary aim was to assess the use of a comprehensive cohort research design (i.e. a parallel patient-preference group alongside a randomised group) in improving the accuracy of this estimate within an orthopaedic trauma setting. Methods: Pragmatic randomised controlled trial and comprehensive cohort study within a level 1 trauma centre. Twenty randomised participants (10 operative and 10 non-operative) and 29 preference participants (3 operative and 26 non-operative). The ge range was 22-72 years and 37 of the 52 patients were men. All participants had an acute rupture of their Achilles tendon and no other injuries. All of the patients in the operative group had a simple end-to-end repair of the tendon with no augmentation. Both groups then followed the same eight-week immediate weight-bearing rehabilitation programme using an off-the-shelf orthotic. The disability rating index (DRI; primary outcome), EQ-5D, Achilles Total Rupture Score and complications were assessed ed at two weeks, six weeks, three months, six months and nine months after initial injury. Results: At nine months, there was no significant difference in DRI between patients randomised to operative or non-operative management. There was no difference in DRI between the randomised group and the parallel patient preference group. The use of a comprehensive cohort of patients did not provide useful additional information as to the treatment effect size because the majority of patients chose non-operative management. Conclusions: Recruitment to clinical trials that compare operative and non-operative interventions is notoriously difficult; especially within the trauma setting. Including a parallel patient preference group to create a comprehensive cohort of patients has been suggested as a way of increasing the power of such trials. In our study, the comprehensive cohort model doubled the number of patients involved in the study. However, a strong preference for non-operative treatment meant that the increased number of patients did not significantly increase the ability of the trial to detect a difference between the two interventions

Temperature time series forecasting in The Optimal Challenges in Irrigation (TO CHAIR)

Predicting and forecasting weather time series has always been a difficult field of research analysis with a very slow progress rate over the years. The main challenge in this project—The Optimal Challenges in Irrigation (TO CHAIR)—is to study how to manage irrigation problems as an optimal control problem: the daily irrigation problem of minimizing water consumption. For that it is necessary to estimate and forecast weather variables in real time in each monitoring area of irrigation. These time series present strong trends and high-frequency seasonality. How to best model and forecast these patterns has been a long-standing issue in time series analysis. This study presents a comparison of the forecasting performance of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA errors, Trend and Seasonal Components) and regression with correlated errors models. These methods are chosen due to their ability to model trend and seasonal fluctuations present in weather data, particularly in dealing with time series with complex seasonal patterns (multiple seasonal patterns). The forecasting performance is demonstrated through a case study of weather time series: minimum air temperature.publishe

Heterogeneities in leishmania infantum infection : using skin parasite burdens to identify highly infectious dogs

Background: The relationships between heterogeneities in host infection and infectiousness (transmission to arthropod vectors) can provide important insights for disease management. Here, we quantify heterogeneities in Leishmania infantum parasite numbers in reservoir and non-reservoir host populations, and relate this to their infectiousness during natural infection. Tissue parasite number was evaluated as a potential surrogate marker of host transmission potential. Methods: Parasite numbers were measured by qPCR in bone marrow and ear skin biopsies of 82 dogs and 34 crab-eating foxes collected during a longitudinal study in Amazon Brazil, for which previous data was available on infectiousness (by xenodiagnosis) and severity of infection. Results: Parasite numbers were highly aggregated both between samples and between individuals. In dogs, total parasite abundance and relative numbers in ear skin compared to bone marrow increased with the duration and severity of infection. Infectiousness to the sandfly vector was associated with high parasite numbers; parasite number in skin was the best predictor of being infectious. Crab-eating foxes, which typically present asymptomatic infection and are non-infectious, had parasite numbers comparable to those of non-infectious dogs. Conclusions: Skin parasite number provides an indirect marker of infectiousness, and could allow targeted control particularly of highly infectious dogs

The Precursors and Products of Justice Climates: Group Leader Antecedents and Employee Attitudinal Consequences

Drawing on the organizational justice, organizational climate, leadership and personality, and social comparison theory literatures, we develop hypotheses about the effects of leader personality on the development of three types of justice climates (e.g., procedural, interpersonal, and informational), and the moderating effects of these climates on individual level justice- attitude relationships. Largely consistent with the theoretically-derived hypotheses, the results showed that leader (a) agreeableness was positively related to procedural, interpersonal and informational justice climates, (b) conscientiousness was positively related to a procedural justice climate, and (c) neuroticism was negatively related to all three types of justice climates. Further, consistent with social comparison theory, multilevel data analyses revealed that the relationship between individual justice perceptions and job attitudes (e.g., job satisfaction, commitment) was moderated by justice climate such that the relationships were stronger when justice climate was high

Genetic algorithm in chemistry.

Genetic algorithm is an optimization technique based on Darwin evolution theory. In last years its application in chemistry is increasing significantly due the special characteristics for optimization of complex systems. The basic principles and some further modifications implemented to improve its performance are presented, as well as a historical development. A numerical example of a function optimization is also shown to demonstrate how the algorithm works in an optimization process. Finally several chemistry applications realized until now is commented to serve as parameter to future applications in this field.22340541

Stochastic Models and Statistical Inference In Evolutionary Genetics: Using DNA Sequence Data To Learn About Population Divergence And Speciation

During speciation, the degree of clustering of a population in terms of genetic polymorphisms increases gradually until the exchange of genes between subpopulations is no longer possible. The isolation-with-migration (IM) model is used to estimate how long ago an ancestral population divided into two subpopulations, and to infer the level of gene flow between the subpopulations during genetic divergence. Its assumption of constant gene flow until the present is however particularly unrealistic in the context of two present-day species. In addition, traditional methods to fit the IM model are aimed at large numbers of DNA sequences from a small number of loci, and are computationally very expensive. To overcome these limitations, this thesis begins by focusing on an extension of the IM model in which the initial period of gene flow is followed by a period of isolation: the so-called isolation-with-initial-migration (IIM) model. For an IIM model with potentially asymmetric gene flow and unequal subpopulation sizes, the distribution of the number of nucleotide differences between two homologous DNA sequences is derived. Based on this distribution, we develop a maximum-likelihood estimation method which is appropriate for data sets containing observations from many independent loci, and is both very efficient and able to deal with mutation rate heterogeneity. Using a data set of Drosophila sequences from approximately 30,000 loci, we show how alternative models, representing different evolutionary scenarios, can be distinguished by means of likelihood ratio tests. To enable inference on both historical and contemporary rates of gene flow between two closely related species, our estimation method is extended to a generalised IM (GIM) model, in which gene flow rates and population sizes can change at some point in the past. Finally, we show how the theory of statistical inference under model misspecification can be used to improve the accuracy of interval estimation and comparison of speciation models; and we develop a simulation method to estimate the limiting distribution of the likelihood ratio statistic when the true parameter vector lies on the boundary of the parameter space

Adenosine-guided pulmonary vein isolation versus conventional pulmonary vein isolation in patients undergoing atrial fibrillation ablation: An updated meta-analysis

BACKGROUND: Recurrent atrial fibrillation episodes following pulmonary vein isolation (PVI) are frequently due to reconnection of PVs. Adenosine can unmask dormant conduction, leading to additional ablation to improve AF-free survival. We performed a meta-analysis of the literature to assess the role of adenosine testing in patients undergoing atrial fibrillation (AF) ablation. METHODS: PubMed, EMBASE, and Cochrane databases were searched through until December 2015 for studies reporting on the role of adenosine guided-PVI versus conventional PVI in AF ablation. RESULTS: Eleven studies including 4099 patients undergoing AF ablation were identified to assess the impact of adenosine testing. Mean age of the population was 61 ± 3 years: 25% female, 70% with paroxysmal AF. Follow up period of 12.5 ± 5.1 months. A significant benefit was observed in the studies published before 2013 (OR = 1.75; 95%CI 1.32–2.33, p < 0.001, I2 = 11%), retrospective (OR = 2.05; 95%CI 1.47–2.86, p < 0.001, I2 = 0%) and single-centre studies (OR = 1.58; 95%CI 1.19–2.10, p = 0.002, I2 = 30%). However, analysis of studies published since 2013 (OR = 1.41; 95% CI 0.87–2.29, p = 0.17, I2 = 75%) does not support any benefit from an adenosine-guided strategy. Similar findings were observed by pooling prospective case-control (OR = 1.39; 95%CI 0.93–2.07, p = 0.11, I2 = 75%), and prospective randomized controlled studies (OR = 1.62; 95%CI 0.81–3.24, p = 0.17, I2 = 86%). Part of the observed high heterogeneity can be explained by parameters such as dormant PVs percentage, use of new technology, improvement of center/operator experience, patients' characteristics including gender, age, and AF type. CONCLUSIONS: Pooling of contemporary data from high quality prospective case–control & prospective randomized controlled studies fails to show the benefit of adenosine-guided strategy to improve AF ablation outcomes