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Publisher Copyright: CCBY Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophisticated algorithms for extraction and clustering of multipath components, following which, point estimates of the model parameters can be obtained using specialized estimators. We propose a likelihood-free calibration method using approximate Bayesian computation. The method is based on the maximum mean discrepancy, which is a notion of distance between probability distributions. Our method not only by-passes the need to implement any high-resolution or clustering algorithm, but is also automatic in that it does not require any additional input or manual pre-processing from the user. It also has the advantage of returning an entire posterior distribution on the value of the parameters, rather than a simple point estimate. We evaluate the performance of the proposed method by fitting two different stochastic channel models, namely the Saleh-Valenzuela model and the propagation graph model, to both simulated and measured data. The proposed method is able to estimate the parameters of both the models accurately in simulations, as well as when applied to 60 GHz indoor measurement data.

Current procedures for inferring population history generally assume complete neutrality���that is, they neglect both direct selection and the effects of selection on linked sites.We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically.We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the distribution of fitness effect as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy. Key words: demographic inference, background selection, distribution of fitness effects, MSMC, fastsimcoal2, approximate Bayesian computation (ABC).

A better understanding of long-term effects of climate and historical anthropogenic changes is needed to define effective conservation measures of endangered forest inhabiting managed landscapes. Diversification and distribution of Mediterranean firs are attributed to the global climate change during the Miocene and Quaternary as well as to the effects of human-driven deforestation. We evaluated the impact of climate change and anthropogenic activities in shaping the genetic diversity and structure of Abies pinsapo Boiss. (Pinaceae), a relict fir endemic from SW Spain. We genotyped a total of 440 individuals from 44 populations by using two different molecular markers (cpSSRs and nSSRs). Overall, low genetic structure was found; however, incipient differentiation appeared within mountain ranges. Analyses suggest that the effects of isolation by distance and lithological or topographical diversity were not enough to structure the populations of the different mountain ranges. The combined role of genetic drift in the small populations and the anthropogenic action associated with forest management has shaped the current genetic pattern of this fir species in the study area. Demographic inference analyses pointed to a very recent synchronic divergence (eleventh–sixteenth century) of the ancestral A. pinsapo population into its current scattered distribution range. Although population bottlenecks were supported by several analyses, the conservation of this endangered species seems not to be limited by lacking genetic diversity, while threats of current climate change and habitat loss must be regarded. Ministerio de Economía y Competitividad P08-RNM-03703, P18-RT-1170, CGL2013-45463-P Junta de Andalucía IF/01375/2012, PTDC/BIA-BIC/5233/2014, POCI-01-0145- FEDER-016817

Despite the huge body of research on osteogenic differentiation and bone tissue engineering, the translation potential of in vitro results still does not match the effort employed. One reason might be that the protocols used for in vitro research have inherent pitfalls. The synthetic glucocorticoid dexamethasone is commonly used in protocols for trilineage differentiation of human bone marrow mesenchymal stromal cells (hBMSCs). However, in the case of osteogenic commitment, dexamethasone has the main pitfall of inhibiting terminal osteoblast differentiation, and its pro-adipogenic effect is well known. In this work, we aimed to clarify the role of dexamethasone in the osteogenesis of hBMSCs, with a particular focus on off-target differentiation. The results showed that dexamethasone does induce osteogenic differentiation by inhibiting SOX9 expression, but not directly through RUNX2 upregulation as it is commonly thought. Rather, PPARG is concomitantly and strongly upregulated, leading to the formation of adipocyte-like cells within osteogenic cultures. Limiting the exposure to dexamethasone to the first week of differentiation did not affect the mineralization potential. Gene expression levels of RUNX2, SOX9, and PPARG were simulated using approximate Bayesian computation based on a simplified theoretical model, which was able to reproduce the observed experimental trends but with a different range of responses, indicating that other factors should be integrated to fully understand how dexamethasone influences cell fate. In summary, this work provides evidence that current in vitro differentiation protocols based on dexamethasone do not represent a good model, and further research is warranted in this field.

[Aim] Species with widespread distributions offer excellent opportunities for investigating recent biogeographical patterns across broad scales. Here, we tested the hypothesis that, due to its intermediate geographical location, NW Africa is pivotal in explaining the phylogeographical patterns of taxa with Mediterranean–Macaronesian distributions using a legume species with short generation times., [Location] Mediterranean, with a focus on NW Africa and the Canary Islands., [Taxon] Pitch trefoil (Bituminaria bituminosa)., [Methods] We generated genetic data and performed phylogeographical and demographic analyses at two geographical scales: Mediterranean Basin (MB), using plastid sequences (115 individuals), and Macaronesia, using plastid sequences (182 individuals) and 10 nuclear microsatellite loci (220 individuals). We also performed a literature survey focusing on phylogeographical studies of other circum-Mediterranean taxa., [Results] Northwest Africa was identified as a centre of genetic diversity (19 out of 38 haplotypes) and demographic expansion of B. bituminosa in the MB during the Pleistocene. Our literature review revealed two main phylogeographical patterns in widespread species: pre-Mediterranean evergreen sclerophylls versus Pleistocene facultative-deciduous (including Bituminaria) taxa, but on average, both functional groups show a similar, large genetic diversity (~40% of haplotypes) in NW Africa. At the Macaronesian scale, we found that Canarian Bituminaria is composed of two genetic sublineages that coexist and hybridize on the central islands and in the mainland Macaronesian enclave (Anti-Atlas region). Demographic analyses rejected the progression rule as the model of island colonization but strongly suggested that Anti-Atlas populations are the result of back-colonization from the easternmost islands before the Last Glacial Maximum (LGM)., [Conclusions] Bituminaria displays a pattern of Quaternary eastward expansion in the MB that appears to be paralleled by several members of its functional plant group. Thus, our study reveals a previously undescribed dual role of NW Africa in plant biogeography, acting both as a source of species expansion to the rest of the MB and a LGM refugium of plant populations with a Macaronesian island origin., This study was funded by a Vincenç Mut project (Govern de les Illes Balears and European Social Fund) and a research prize awarded to CG-V by Fundación Amurga.

In the present article we use geometric microliths (a specific type of arrowhead) and Approximate Bayesian Computation (ABC) in order to evaluate possible origin points and expansion routes for the Neolithic in the Iberian Peninsula. In order to do so, we divide the Iberian Peninsula in four areas (Ebro river, Catalan shores, Xu´quer river and Guadalquivir river) and we sample the geometric microliths existing in the sites with the oldest radiocarbon dates for each zone. On this data, we perform a partial Mantel test with three matrices: geographic distance matrix, cultural distance matrix and chronological distance matrix. After this is done, we simulate a series of partial Mantel tests where we alter the chronological matrix by using an expansion model with randomised origin points, and using the distribution of the observed partial Mantel test’s results as a summary statistic within an Approximate Bayesian Computation-Sequential Monte-Carlo (ABC-SMC) algorithm framework. Our results point clearly to a Neolithic expansion route following the Northern Mediterranean, whilst the Southern Mediterranean route could also find support and should be further discussed. The most probable origin points focus on the Xu´quer river area.

We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met. If the condition is sparsely met during the repeated queries, SVT can drastically reduce the cumulative privacy loss, unlike the usual case where every query incurs the privacy loss. In ABC, the quantity of interest is the distance between observed and simulated data, and only when the distance is below a threshold can we take the corresponding prior sample as a posterior sample. Hence, applying SVT to ABC is an organic way to transform an ABC algorithm to a privacy-preserving variant with minimal modification, but yields the posterior samples with a high privacy level. We theoretically analyzed the interplay between the noise added for privacy and the accuracy of the posterior samples. We apply ABCDP to several data simulators and show the efficacy of the proposed framework.

With modern high-throughput technologies, scientists can now collect high-dimensional data of various forms, including brain images, medical spectrum curves, engineering signals, etc. These data provide a rich source of information on disease development, cell evolvement, engineering systems, and many other scientific phenomena. To achieve a clearer understanding of the underlying mechanism, one needs a fast and reliable analytical approach to extract useful information from the wealth of data. The goal of this dissertation is to develop novel methods that enable scalable estimation, testing, and analysis of complex, high-dimensional data. It contains three parts: parameter estimation based on complex data, powerful testing of functional data, and the analysis of functional data supported on manifolds. The first part focuses on a family of parameter estimation problems in which the relationship between data and the underlying parameters cannot be explicitly specified using a likelihood function. We introduce a wavelet-based approximate Bayesian computation approach that is likelihood-free and computationally scalable. This approach will be applied to two applications: estimating mutation rates of a generalized birth-death process based on fluctuation experimental data and estimating the parameters of targets based on foliage echoes. The second part focuses on functional testing. We consider using multiple testing in basis-space via p-value guided compression. Our theoretical results demonstrate that, under regularity conditions, the Westfall-Young randomization test in basis space achieves strong control of family-wise error rate and asymptotic optimality. Furthermore, appropriate compression in basis space leads to improved power as compared to point-wise testing in data domain or basis-space testing without compression. The effectiveness of the proposed procedure is demonstrated through two applications: the detection of regions of spectral curves associated with pre-cancer using 1-dimensional fluorescence spectroscopy data and the detection of disease-related regions using 3-dimensional Alzheimer's Disease neuroimaging data. The third part focuses on analyzing data measured on the cortical surfaces of monkeys' brains during their early development, and subjects are measured on misaligned time markers. In this analysis, we examine the asymmetric patterns and increase/decrease trend in the monkeys' brains across time. Doctor of Philosophy With modern high-throughput technologies, scientists can now collect high-dimensional data of various forms, including brain images, medical spectrum curves, engineering signals, and biological measurements. These data provide a rich source of information on disease development, engineering systems, and many other scientific phenomena. The goal of this dissertation is to develop novel methods that enable scalable estimation, testing, and analysis of complex, high-dimensional data. It contains three parts: parameter estimation based on complex biological and engineering data, powerful testing of high-dimensional functional data, and the analysis of functional data supported on manifolds. The first part focuses on a family of parameter estimation problems in which the relationship between data and the underlying parameters cannot be explicitly specified using a likelihood function. We introduce a computation-based statistical approach that achieves efficient parameter estimation scalable to high-dimensional functional data. The second part focuses on developing a powerful testing method for functional data that can be used to detect important regions. We will show nice properties of our approach. The effectiveness of this testing approach will be demonstrated using two applications: the detection of regions of the spectrum that are related to pre-cancer using fluorescence spectroscopy data and the detection of disease-related regions using brain image data. The third part focuses on analyzing brain cortical thickness data, measured on the cortical surfaces of monkeys’ brains during early development. Subjects are measured on misaligned time-markers. By using functional data estimation and testing approach, we are able to: (1) identify asymmetric regions between their right and left brains across time, and (2) identify spatial regions on the cortical surface that reflect increase or decrease in cortical measurements over time.

Despite the huge body of research on osteogenic differentiation and bone tissue engineering, the translation potential of in vitro results still does not match the effort employed. One reason might be that the protocols used for in vitro research have inherent pitfalls. The synthetic glucocorticoid dexamethasone is commonly used in protocols for trilineage differentiation of human bone marrow mesenchymal stromal cells (hBMSCs). However, in the case of osteogenic commitment, dexamethasone has the main pitfall of inhibiting terminal osteoblast differentiation, and its pro-adipogenic effect is well known. In this work, we aimed to clarify the role of dexamethasone in the osteogenesis of hBMSCs, with a particular focus on off-target differentiation. The results showed that dexamethasone does induce osteogenic differentiation by inhibiting SOX9 expression, but not directly through RUNX2 upregulation as it is commonly thought. Rather, PPARG is concomitantly and strongly upregulated, leading to the formation of adipocyte-like cells within osteogenic cultures. Limiting the exposure to dexamethasone to the first week of differentiation did not affect the mineralization potential. Gene expression levels of RUNX2, SOX9, and PPARG were simulated using approximate Bayesian computation based on a simplified theoretical model, which was able to reproduce the observed experimental trends but with a different range of responses, indicating that other factors should be integrated to fully understand how dexamethasone influences cell fate. In summary, this work provides evidence that current in vitro differentiation protocols based on dexamethasone do not represent a good model, and further research is warranted in this field.

International audience; Regularization and Bayesian inference based methods have been successfully applied for linear inverse problems. In these methods, often simple Gaussian or Poisson models for the forward model errors have been considered. In this work, we use variable splitting for the errors to model different sources of errors and their possible non-stationarity or impulsive nature using Student-t or other heavy tailed distributions. Also, as a prior model, a sparsity enforcing hierarchical model of Infinite Gaussian Mixture model is introduced. With these prior models, we obtain a complete Bayesian inference framework which can efficiently be implemented for any linear inverse problem. Interestingly , many recent regularization-based algorithms such as Alternating Direction Method of Multipliers (ADMM) as well as more classical Bayesian based methods such as Sparse Bayesian Learning (SBL) are obtained as particular cases. One advantage of the Bayesian approach is the possibility to estimate, jointly with the reconstruction, the hyper-parameters such as the regulariza-tion parameter, thus the capability of proposing unsupervised methods. Examples of implementation of the proposed method in different signal and image processing such as deconvolution in mass spectrometry, estimation of periodic components estimation in biological signals and computed tomography are mentioned and referenced.

Linking pattern to process across spatial and temporal scales has been a key goal of the field of biogeography. In January 2017, the 8th biennial conference of the International Biogeography Society sponsored a symposium on Building up biogeography—process to pattern that aimed to review progress towards this goal. Here we present a summary of the symposium, in which we identified promising areas of current research and suggested future research directions. We focus on (1) emerging types of data such as behavioral observations and ancient DNA, (2) how to better incorporate historical data (such as fossils) to move beyond what we term “footprint measures” of past dynamics and (3) the role that novel modelling approaches (e.g. maximum entropy theory of ecology and approximate Bayesian computation) and conceptual frameworks can play in the unification of disciplines. We suggest that the gaps separating pattern and process are shrinking, and that we can better bridge these aspects by considering the dimensions of space and time simultaneously.

Selected reaction monitoring (SRM) has become one of the main methods for low-mass-range–targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies, the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this article, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data. The methodology is likelihood-free, using approximate Bayesian computation implemented via a Markov chain Monte Carlo procedure and a kernel-based Optimal Bayesian Classifier. Extensive experimental results demonstrate that the proposed method outperforms classical methods such as linear discriminant analysis and 3NN, when sample size is small, dimensionality is large, the data are noisy, or a combination of these.

This thesis consists of five papers published in peer-reviewed journals, two of which focus on method development of time-serial inference, represented by publications in methodology journals, and three others that describe collaborative data applications based on experimental evolution projects, which are published in biological journals. The first methodology publication (Chapter 2) is a comparison study of recently-introduced time-serial methods, performed using a systematic approach to evaluate, through simulation, the strengths and weaknesses of each method in quantifying selection coefficients from time-sampled data sets. The second methodology chapter (Chapter 3) introduced a novel method extending Wright-Fisher Approximate Bayesian Computation (WFABC) to detect and quantify changes in selection strength from temporal allele trajectories. This method was particularly novel in its use of simulation-based inference to detect and quantify €˜changes€™ in evolutionary trajectories, which involved the application of Change-Point Analysis. In the first data application (Chapter 4), a time-sampled experiment of echovirus 11 evolved under the disinfectant (chlorine dioxide) was assessed on the associated genotypic and phenotypic trait in collaboration with the Environmental Chemistry Laboratory (LCE) at EPFL. This study contributes to a better understanding of disinfection resistance in waterborne viruses by identifying the mutations associated with enhanced replicative fitness. In the next application (Chapter 5), a comprehensive time-serial analysis was performed on experimental evolution data of echovirus 11 on UVC adaptation in collaboration with the Environmental Chemistry Laboratory (LCE). This study shows that the UVC adaptation of echovirus 11 is associated with a decrease in the virus mutation rate, which is an evidence of the ability of echovirus 11 to adapt to the commonly used disinfectant procedure of UVC radiation in clinical settings and water treatment plants. Additionally, a paper implementing Change-Point WFABC (CP-WFABC) to assess the experimental evolution of influenza A virus is shown in Appendix; specifically, the effects of a novel mutagenic drug (favipiravir) on adaptive allele trajectories were evaluated, with results indicating mutation meltdown under a high drug dosage.

Made available in DSpace on 2018-11-26T17:40:21Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-08-24 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) NASA National Science Foundation Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Brigham Young University Fulton Supercomputing Lab Background: Estimating the variability in isolation times across co-distributed taxon pairs that may have experienced the same allopatric isolating mechanism is a core goal of comparative phylogeography. The use of hierarchical Approximate Bayesian Computation (ABC) and coalescent models to infer temporal dynamics of lineage co-diversification has been a contentious topic in recent years. Key issues that remain unresolved include the choice of an appropriate prior on the number of co-divergence events (psi), as well as the optimal strategies for data summarization. Methods: Through simulation-based cross validation we explore the impact of the strategy for sorting summary statistics and the choice of prior on psi on the estimation of co-divergence variability. We also introduce a new setting (beta) that can potentially improve estimation of psi by enforcing a minimal temporal difference between pulses of co-divergence. We apply this new method to three empirical datasets: one dataset each of co-distributed taxon pairs of Panamanian frogs and freshwater fishes, and a large set of Neotropical butterfly sister-taxon pairs. Results: We demonstrate that the choice of prior on. has little impact on inference, but that sorting summary statistics yields substantially more reliable estimates of co-divergence variability despite violations of assumptions about exchangeability. We find the implementation of beta improves estimation of psi, with improvement being most dramatic given larger numbers of taxon pairs. We find equivocal support for synchronous co-divergence for both of the Panamanian groups, but we find considerable support for asynchronous divergence among the Neotropical butterflies. Conclusions: Our simulation experiments demonstrate that using sorted summary statistics results in improved estimates of the variability in divergence times, whereas the choice of hyperprior on psi has negligible effect. Additionally, we demonstrate that estimating the number of pulses of co-divergence across co-distributed taxon-pairs is improved by applying a flexible buffering regime over divergence times. This improves the correlation between. and the true variability in isolation times and allows for more meaningful interpretation of this hyperparameter. This will allow for more accurate identification of the number of temporally distinct pulses of co-divergence that generated the diversification pattern of a given regional assemblage of sister-taxon-pairs. CUNY City Coll, Dept Biol, New York, NY 10031 USA CUNY, Grad Ctr, New York, NY 10016 USA Univ Brasilia, Dept Zool, BR-70910900 Brasilia, DF, Brazil Univ Estadual Paulista, IBiLCE, Dept Zool & Bot, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil Univ Estadual Paulista, IBiLCE, Dept Zool & Bot, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil FAPESP: 2013/50297-0 NASA: DOB 1343578 National Science Foundation: DEB-1253710 National Science Foundation: CNS-0958379 National Science Foundation: CNS-0855217 CNPq: 314724/2014-1

Admixture between differentiated populations is considered to be a powerful mechanism stimulating the invasive success of some introduced species. It is generally facilitated through multiple introductions; however, the importance of admixture prior to introduction has rarely been considered. We assess the likelihood that the invasive Ambrosia artemisiifolia populations of Europe and Australia developed through multiple introductions or were sourced from a historical admixture zone within native North America. To do this,, we combine large genomic and sampling datasets analyzed with approximate Bayesian computation and Random Forest scenario evaluation to compare single and multiple invasion scenarios with pre- and post-introduction admixture simultaneously. We show the historical admixture zone within native North America originated before global invasion of this weed, and could act as a potential source of introduced populations. We provide evidence supporting the hypothesis that the invasive populations established through multiple introductions from the native range into Europe and subsequent bridgehead invasion into Australia. We discuss the evolutionary mechanisms that could promote invasiveness and evolutionary potential of alien species from bridgehead invasions and admixed source populations. This article is protected by copyright. All rights reserved.

Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the 'coverage test' with which accuracy is assessed. We apply this method - which we call 'error-calibrated ABC' - to a toy example and a realistic 14-parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic 'coverage test' show that our approach improves estimation of parameter values and their credible intervals for both models.

The remarkable complexity of modern applied problems often requires the use of probabilistic models where the likelihood is intractable -- in the sense that it cannot be numerically evaluated, not even up to a normalizing constant. The statistical literature provides an extensive array of methods designed to bypass this constraint. Still, inference in this context remains computationally challenging, particularly for high-dimensional models. We focus on the important class of Approximation Bayesian Computation (ABC) methods. Various state-of-the-art ABC techniques are combined to fit an intractable model that describes the epidemiological dynamics of multidrug-resistant tuberculosis. This study addresses a number of important biological questions in a principled manner, providing useful insights to this extraordinarily relevant research topic. We propose a functional regression adjustment ABC procedure that permits the estimation of infinite-dimensional parameters, which effectively launches ABC into the non-parametric framework. Two likelihood-free algorithms are also introduced. The first exploits the principles of ABC and the so-called coverage property to recalibrate an auxiliary approximate posterior estimator. This approach further strengthens the links between ABC and indirect inference, allowing a more comprehensive use of the auxiliary estimator. The second algorithm employs the ABC machinery to build approximate samplers for the intractable full conditional distributions. These samplers are then combined to form a likelihood-free approximate Gibbs sampler. The granular nature of our approach (that comes from breaking down the problem into small pieces) makes it suitable for highly-structured problems. We demonstrate this property by fitting an intractable and very high-dimensional state space model.

The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature. We consider different types of boosting and compare them to partial least-squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the reintroduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θanc = 4Neu) and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L2-loss performs best. Applying that method to the ibex data, we estimate θ^anc≈1.288 and find that most of the variation across loci of the ancestral mutation rate u is between 7.7 × 10−4 and 3.5 × 10−3 per locus per generation. The proportion of males with access to matings is estimated as ω^≈0.21, which is in good agreement with recent independent estimates.

Despite the large body of research devoted to understanding the role of Quaternary glacial cycles in the genetic divergence of European trees, the differential contribution of geographic isolation and/or environmental adaptation in creating population genetic divergence remains unexplored. In this study, we used a long-lived tree (Taxus baccata) as a model species to investigate the impact of Quaternary climatic changes on genetic diversity via neutral (isolation-by-distance) and selective (isolation-by-adaptation) processes. We applied approximate Bayesian computation to genetic data to infer its demographic history, and combined this information with past and present climatic data to assess the role of environment and geography in the observed patterns of genetic structure. We found evidence that yew colonized Europe from the East, and that European samples diverged into two groups (Western, Eastern) at the beginning of the Quaternary glaciations, c. 2.2 Myr before present. Apart from the expected effects of geographical isolation during glacials, we discovered a significant role of environmental adaptation during interglacials at the origin of genetic divergence between both groups. This process may be common in other organisms, providing new research lines to explore the effect of Quaternary climatic factors on present-day patterns of genetic diversity. © 2015 New Phytologist Trust.

Background When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. Methods We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). Results In the estimation of the standard deviation, our ABC method performs better than the other methods when data are generated from skewed or heavy-tailed distributions. The corresponding average relative error (ARE) approaches zero as sample size increases. In data generated from the normal distribution, our ABC performs well. However, the Wan et al. method is best for estimating standard deviation under normal distribution. In the estimation of the mean, our ABC method is best regardless of assumed distribution. Conclusion ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95 % credible interval when Bayesian analysis has been employed. Electronic supplementary material The online version of this article (doi:10.1186/s12874-015-0055-5) contains supplementary material, which is available to authorized users.

Monogenic plant resistance breakdown is a model for testing evolution in action in pathogens. As a rule, plant pathologists argue that virulence - the allele that allows pathogens to overcome resistance - is due to a new mutation at the avirulence locus within the native/endemic population that infects susceptible crops. In this article, we develop an alternative and neglected scenario where a given virulence pre-exists in a non-agricultural host and might be accidentally released or introduced on the matching resistant cultivar in the field. The main difference between the two scenarios is the divergence time expected between the avirulent and the virulent populations. As a consequence, population genetic approaches such as genome scans and Approximate Bayesian Computation methods allow explicit testing of the two scenarios by timing the divergence. This review then explores the fundamental implications of this alternative scenario for plant breeding, including the invasion of virulence or the evolution of more aggressive hybrids, and proposes concrete solutions to achieve a sustainable resistance. (C) 2014 Elsevier B.V. All rights reserved.

La ciudad de Bogotá presenta problemas de drenaje urbano ocasionados por los excesos de precipitación que no son evacuados adecuadamente. En el contexto de esta problemática, existe una variedad de alternativas para el control de estos excesos: los Sistemas de Drenaje Urbano Sostenible (SUDS). En esta tesis se investigan las cubiertas verdes (CV) como alternativa para el control de la escorrentía urbana. Las CV se clasifican como intensivas o extensivas, dependiendo de su vegetación, tipo y profundidad de sustrato. Para el caso de esta investigación se implementaron helechos y sedums, por ser un tipo de vegetación resistente a cambios de temperatura, adaptabilidad a condiciones extremas del clima como periodos de invierno y sequía, y por su capacidad de sobrevivencia en sustratos áridos similares a aquellos con materiales reciclados de la construcción. En cuanto al sustrato seleccionado para las CV, se utilizaron materiales reciclados de la construcción, tales como ladrillo triturado, arena de río, humus, tierra, escoria de carbón y cascarilla de arroz. La selección de estos materiales se realizó teniendo en cuenta el uso potencial de materiales derivados de la construcción como sustrato en la instalación de SUDS. Estos materiales provienen de los escombros que se generan en la ciudad de Bogotá y esta es una alternativa para la disminución del volumen de estos residuos al darles una vida útil. El periodo de recolección de información fue desde el 3 de marzo de 2012 hasta el 5 de mayo de 2013, periodo durante el cual se logró capturar un total de 35 tormentas. Los resultados de los eventos de precipitación se evaluaron para 4 cubiertas verdes (2 sedums y 2 helechos) con inclinaciones del 2% y 5%; la cubierta de comparación fue una en policarbonato con una pendiente del 2%. En promedio, se registró un 75% de capacidad de atenuación máxima en las cubiertas intensivas (helechos) y un 64% en las extensivas (sedums). La evaluación estadística de los resultados observados se realizó con la prueba ANOVA, y se encontró una diferencia estadística entre el control y las cubiertas verdes para un nivel de significancia del 5% (valor-p 0.05). En el desarrollo de la modelación numérica, a través del Cálculo Aproximado Bayesiano (CAB), se definieron las densidades de probabilidad características de los parámetros de contenido volumétrico de agua residual (or), contenido volumétrico de saturación(ks), conductividad hidráulica en estado de saturación (Ks) y los parámetros (α) y (n) particulares de los sustratos instalados. Para propósitos de modelación numérica se propone la implementación del Índice de Retención de Humedad (IRH) como mecanismo de calibración del modelo de flujo propuesto. Abstract. Bogotá presents urban drainage problems caused by excessive precipitation which is not adequately evacuated. In the context of this problem, Sustainable Urban Drainage Systems (SUDS) represent an alternative to reduce storm water excesses. In this thesis, green roofs (GR) are investigated as an alternative to urban runoff management. GR are classified as intensive or extensive, depending on the vegetation, substrate type and depth. For the case in hand, ferns and sedums were implemented for being a type of vegetation resistant to temperature changes, adapting to extreme weather conditions such as drought and winter periods, and because off their ability to survive on dry substrates like those with recycled construction. Recycled construction materials, also known as construction waste material, were used as the substrate selected for the CV such as: crushed brick, river sand, humus, soil, coal slag and rice husk. These materials were selected considering the potential use of materials from construction as a substrate in the installation of SUDS. These materials come from rubbles originated in the city of Bogotá and this is an alternative to reduce its volume by giving it a useful purpose. The study period was from March 3, 2012 to May 5, 2013, and a total of 35 precipitation events were captured. The events were evaluated for 4 green roofs (2 sedums and 2 ferns) with slopes of 2% and 5%; and a polycarbonate roof with a slope of 2% was used for comparison purposes. On average, we found a 75% peak flow reduction for intensive roofs (ferns) and a 64% peak flow reduction for extensive roofs (sedums). An ANOVA test was performed to evaluate statistical differences among green roofs and the polycarbonate roof. We found only a statistical difference between the green roofs and the polycarbonate roof for a significance level of 0.05 (p-value 0.05). A numerical model was developed to estimate effluent flows from the green roofs. The model was calibrated through an Approximate Bayesian Computation (ABC) statistical approach. Posterior probability distributions for the unsaturated soil parameters (Or, Ks, α, n, Ks, y σ) are reported. We proposed the water retention index (WRI) as a hydraulic signature to capture the flow dynamics within the green roof soils. Maestría