13 research outputs found

    Statistical Population Genomics

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    This open access volume presents state-of-the-art inference methods in population genomics, focusing on data analysis based on rigorous statistical techniques. After introducing general concepts related to the biology of genomes and their evolution, the book covers state-of-the-art methods for the analysis of genomes in populations, including demography inference, population structure analysis and detection of selection, using both model-based inference and simulation procedures. Last but not least, it offers an overview of the current knowledge acquired by applying such methods to a large variety of eukaryotic organisms. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, pointers to the relevant literature, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Statistical Population Genomics aims to promote and ensure successful applications of population genomic methods to an increasing number of model systems and biological questions

    Phylogenetics in the Genomic Era

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    Molecular phylogenetics was born in the middle of the 20th century, when the advent of protein and DNA sequencing offered a novel way to study the evolutionary relationships between living organisms. The first 50 years of the discipline can be seen as a long quest for resolving power. The goal – reconstructing the tree of life – seemed to be unreachable, the methods were heavily debated, and the data limiting. Maybe for these reasons, even the relevance of the whole approach was repeatedly questioned, as part of the so-called molecules versus morphology debate. Controversies often crystalized around long-standing conundrums, such as the origin of land plants, the diversification of placental mammals, or the prokaryote/eukaryote divide. Some of these questions were resolved as gene and species samples increased in size. Over the years, molecular phylogenetics has gradually evolved from a brilliant, revolutionary idea to a mature research field centred on the problem of reliably building trees. This logical progression was abruptly interrupted in the late 2000s. High-throughput sequencing arose and the field suddenly moved into something entirely different. Access to genome-scale data profoundly reshaped the methodological challenges, while opening an amazing range of new application perspectives. Phylogenetics left the realm of systematics to occupy a central place in one of the most exciting research fields of this century – genomics. This is what this book is about: how we do trees, and what we do with trees, in the current phylogenomic era. One obvious, practical consequence of the transition to genome-scale data is that the most widely used tree-building methods, which are based on probabilistic models of sequence evolution, require intensive algorithmic optimization to be applicable to current datasets. This problem is considered in Part 1 of the book, which includes a general introduction to Markov models (Chapter 1.1) and a detailed description of how to optimally design and implement Maximum Likelihood (Chapter 1.2) and Bayesian (Chapter 1.4) phylogenetic inference methods. The importance of the computational aspects of modern phylogenomics is such that efficient software development is a major activity of numerous research groups in the field. We acknowledge this and have included seven "How to" chapters presenting recent updates of major phylogenomic tools – RAxML (Chapter 1.3), PhyloBayes (Chapter 1.5), MACSE (Chapter 2.3), Bgee (Chapter 4.3), RevBayes (Chapter 5.2), Beagle (Chapter 5.4), and BPP (Chapter 5.6). Genome-scale data sets are so large that statistical power, which had been the main limiting factor of phylogenetic inference during previous decades, is no longer a major issue. Massive data sets instead tend to amplify the signal they deliver – be it biological or artefactual – so that bias and inconsistency, instead of sampling variance, are the main problems with phylogenetic inference in the genomic era. Part 2 covers the issues of data quality and model adequacy in phylogenomics. Chapter 2.1 provides an overview of current practice and makes recommendations on how to avoid the more common biases. Two chapters review the challenges and limitations of two key steps of phylogenomic analysis pipelines, sequence alignment (Chapter 2.2) and orthology prediction (Chapter 2.4), which largely determine the reliability of downstream inferences. The performance of tree building methods is also the subject of Chapter 2.5, in which a new approach is introduced to assess the quality of gene trees based on their ability to correctly predict ancestral gene order. Analyses of multiple genes typically recover multiple, distinct trees. Maybe the biggest conceptual advance induced by the phylogenetic to phylogenomic transition is the suggestion that one should not simply aim to reconstruct “the” species tree, but rather to be prepared to make sense of forests of gene trees. Chapter 3.1 reviews the numerous reasons why gene trees can differ from each other and from the species tree, and what the implications are for phylogenetic inference. Chapter 3.2 focuses on gene trees/species trees reconciliation methods that account for gene duplication/loss and horizontal gene transfer among lineages. Incomplete lineage sorting is another major source of phylogenetic incongruence among loci, which recently gained attention and is covered by Chapter 3.3. Chapter 3.4 concludes this part by taking a user’s perspective and examining the pros and cons of concatenation versus separate analysis of gene sequence alignments. Modern genomics is comparative and phylogenetic methods are key to a wide range of questions and analyses relevant to the study of molecular evolution. This is covered by Part 4. We argue that genome annotation, either structural or functional, can only be properly achieved in a phylogenetic context. Chapters 4.1 and 4.2 review the power of these approaches and their connections with the study of gene function. Molecular substitution rates play a key role in our understanding of the prevalence of nearly neutral versus adaptive molecular evolution, and the influence of species traits on genome dynamics (Chapter 4.4). The analysis of substitution rates, and particularly the detection of positive selection, requires sophisticated methods and models of coding sequence evolution (Chapter 4.5). Phylogenomics also offers a unique opportunity to explore evolutionary convergence at a molecular level, thus addressing the long-standing question of predictability versus contingency in evolution (Chapter 4.6). The development of phylogenomics, as reviewed in Parts 1 through 4, has resulted in a powerful conceptual and methodological corpus, which is often reused for addressing problems of interest to biologists from other fields. Part 5 illustrates this application potential via three selected examples. Chapter 5.1 addresses the link between phylogenomics and palaeontology; i.e., how to optimally combine molecular and fossil data for estimating divergence times. Chapter 5.3 emphasizes the importance of the phylogenomic approach in virology and its potential to trace the origin and spread of infectious diseases in space and time. Finally, Chapter 5.5 recalls why phylogenomic methods and the multi-species coalescent model are key in addressing the problem of species delimitation – one of the major goals of taxonomy. It is hard to predict where phylogenomics as a discipline will stand in even 10 years. Maybe a novel technological revolution will bring it to yet another level? We strongly believe, however, that tree thinking will remain pivotal in the treatment and interpretation of the deluge of genomic data to come. Perhaps a prefiguration of the future of our field is provided by the daily monitoring of the current Covid-19 outbreak via the phylogenetic analysis of coronavirus genomic data in quasi real time – a topic of major societal importance, contemporary to the publication of this book, in which phylogenomics is instrumental in helping to fight disease

    A brief history of learning classifier systems: from CS-1 to XCS and its variants

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    © 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning

    Ein Smart Home Management-System basierend auf adaptive Lernalgorithmen des Verhaltenserwerbs (ENKOS)

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    Mit dem adaptiven Energie- und Komfortmanagementsystem „ENKOS“ auf Basis von Learning Classifier Systems wird die Benutzung elektrischer Geräte in einem Haushalt adaptiv gelernt. Diese werden zur Steigerung des Wohnkomforts durch eine intelligente Vorhersage eingesetzt, womit ein völlig neuer Ansatz eines „Smart Home“-Systems realisiert wird. Außerdem wird damit eine signifikante Energiereduzierung erreicht. Beide Ziele, die Komfortsteigerung sowie die Energieminimierung in einem standardisierten Haushalt, wurden in eine Zielfunktion integriert und mit Hilfe von verhaltenspsychologischen Methoden quantifiziert. Danach wurde die Topologie von ENKOS, dem zentralen Lern- und Steuersystem der elektrischen Geräte, aus mehreren Möglichkeiten ausgewählt. Die Zielvariante wurde „Kognitive Learning Classifier Systems“ (KLCS) genannt, da die LCS die Grundlage des Ansatzes bilden und um Methoden der kognitiven Verhaltensforschung erweitert wurden.In the present work it has been able to develop an adaptive learning system "ENKOS" ("Energy and Comfort management system") basing on Learning Classifier Systems to adopt human behavior in relation to the use of electrical appliances in a household. This could be used to improve living comfort through an intelligent prediction. The learning algorithm refers to a correction of a decision made by ENKOS by the user and as well as the correction time, and so realizes an adaptive "smart home" system with the help of a completely new approach. The information got by the usage patterns of electrical equipment by users are also used for the construction of logical and electrical models of the apartment, which could be used for a significant energy reduction in the household. Both of these goals, the increase of living comfort and the energy minimization in a standardized household were integrated into an objective function and thus quantified. For accurate parametrization of the objective function, behavioral psychological methods of human decision-making have been used to convert the objective control strategy of all devices in the simulated household into the subjective perception of people. Thereafter, the topology of ENKOS, the central learning and control system of the electrical devices were selected from several options. In particular, for the integration of simple physical models into a complex behavior model stored in the LCS several fundamental issues were discussed to generate a final overall topology. The target variant was named “Cognitive Learning Classifier Systems” (KLCS) derived form the basis of the approach, the LCS, extended by the methods to adopt human decision making. After setting up the structure of the system, a standard simulation environment was developed to evaluate different parameters of the created KLCS objectively in terms of maximizing the objective function. This revealed that two out of three tested methods of energy minimization and only two of six parameters of the LCS components are having a significant impact on the target function value. It was shown that the established system ENKOS could both, an increase in comfort for the user and a significant reduction of energy consumption in the simulated standard apartment. With ENKOS a real adaptive smart home system was created to help the user in daily living.Mit der vorliegenden Arbeit ist es gelungen, ein adaptives Lernsystem „ENKOS“ („Energie- und Komfortmanagementsystem“) auf Basis von Learning Classifier Systems zu entwickeln, um menschliche Verhaltensweisen in Bezug auf die Benutzung elektrischer Geräte in einem Haushalt zu lernen. Diese konnten für die Steigerung des Wohnkomforts durch eine intelligente Vorhersage eingesetzt werden. Der Lernalgorithmus bezieht zum einen die Korrektur der Entscheidung von ENKOS durch den Nutzer und zum anderen die Korrekturzeit ein und realisiert damit adaptives „Smart Home“-System mit einem völlig neuen Ansatz. Die Informationen, die durch die Benutzungsmuster von elektrischen Geräten durch die Nutzer erhoben werden können, werden außerdem zum Aufbau von logischen und elektrischen Modellen der Wohnung benutzt, womit eine signifikante Energiereduzierung erreicht werden konnte. Beide Ziele, die Komfortsteigerung sowie die Energieminimierung in einem standardisierten Haushalt, wurden in eine Zielfunktion integriert und damit quantifiziert. Für die Anpassung der Zielfunktion wurden auch verhaltenspsychologische Methoden der menschlichen Entscheidungsfindung herangezogen, um die objektive Steuerstrategie aller Geräte im simulierten Haushalt in die subjektive Empfindung von Menschen überführen zu können. Danach wurde die Topologie von ENKOS, dem zentralen Lern- und Steuersystem der elektrischen Geräte, aus mehreren Möglichkeiten ausgewählt. Insbesondere für die Integration der einfachen physikalischen Modelle in ein komplexes Verhaltensmodell (abgebildet durch die LCS-Regeln) wurden mehrere grundsätzliche Fragestellungen gegeneinander abgewogen, um eine entsprechende Gesamttopologie zu generieren. Die Zielvariante wurde „Kognitive Learning Classifier Systems“ (KLCS) genannt, da die LCS die Grundlage des Ansatzes bilden und um Methoden der kognitiven Verhaltensforschung erweitert wurden. Nach dem Aufstellen der Systemstruktur wurde eine Standardsimulationsumgebung geschaffen, um verschiedene Parameter des geschaffenen KLCS objektiv bewerten zu können, um die Zielfunktion zu maximieren. Dabei kam heraus, dass zwei von drei untersuchten Methoden der Energieminimierung aussichtsreich sind, wobei bei der Parametrisierung der LCS-Komponenten lediglich zwei von sechs Parametern einen signifikanten Einfluss auf den Zielfunktionswert haben. Es konnte gezeigt werden, dass das aufgestellte System ENKOS sowohl eine Steigerung des Komforts für die Nutzer ermöglicht sowie die signifikante Reduzierung des Energieverbrauchs in der simulierten Standardwohnung realisiert. Damit ist ein wirklich adaptives Smart Home-System im Sinne des Anwenders als Ansatz geschaffen und evaluiert worden

    Distribution de la diversité génétique dans les réseaux dendritiques : patrons, processus et implications pour la conservation de la biodiversité

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    L'objectif de cette thèse est de caractériser la distribution de la diversité génétique dans les réseaux dendritiques. Premièrement, nous identifions un patron de diversité génétique dans ces réseaux, ainsi que les effets de l'asymétrie de flux de gènes, les différences de tailles efficaces, et les processus de colonisation sur ces patrons. Deuxièmement, nous caractérisons les patrons de diversité génétique de quatre espèces de poissons (Gobio occitaniae, Squalius cephalus, Barbatula barbatula et Phoxinus phoxinus) dans le bassin de la Garonne, afin d'identifier des zones à protéger. Troisièmement, nous explorons les effets de l'asymétrie de flux de gènes sur l'inférence des histoires démographiques des populations. Finalement, nous combinons des méthodes génétiques et démographiques pour évaluer le statut d'une espèce menacée (Parachondrostoma toxostoma).The objective of this thesis is to characterize the distribution of genetic diversity in dendritic networks. First, we identify a general spatial pattern of genetic diversity on these ecosystems, as well as the effects of asymmetric gene flow, differential in effective population sizes and colonization processes on this pattern. Second, we characterize patterns of genetic diversity of four freshwater fish species (Gobio occitaniae, Squalius cephalus, Barbatula barbatula and Phoxinus phoxinus) at the Garonne river basin, so as to identify priority areas to protect. Third, we explore the effects of gene flow asymmetry on the inference of populations' demographic histories. Finally, we combine genetic and demographic approaches to evaluate the status of a threathened species (Parachondrostoma toxostoma)

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Never Too Old To Learn: On-line Evolution of Controllers in Swarm- and Modular Robotics

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    Eiben, A.E. [Promotor

    11th International Coral Reef Symposium Abstracts

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    https://nsuworks.nova.edu/occ_icrs/1001/thumbnail.jp

    11th International Coral Reef Symposium Proceedings

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    A defining theme of the 11th International Coral Reef Symposium was that the news for coral reef ecosystems are far from encouraging. Climate change happens now much faster than in an ice-age transition, and coral reefs continue to suffer fever-high temperatures as well as sour ocean conditions. Corals may be falling behind, and there appears to be no special silver bullet remedy. Nevertheless, there are hopeful signs that we should not despair. Reef ecosystems respond vigorously to protective measures and alleviation of stress. For concerned scientists, managers, conservationists, stakeholders, students, and citizens, there is a great role to play in continuing to report on the extreme threat that climate change represents to earth’s natural systems. Urgent action is needed to reduce CO2 emissions. In the interim, we can and must buy time for coral reefs through increased protection from sewage, sediment, pollutants, overfishing, development, and other stressors, all of which we know can damage coral health. The time to act is now. The canary in the coral-coal mine is dead, but we still have time to save the miners. We need effective management rooted in solid interdisciplinary science and coupled with stakeholder buy in, working at local, regional, and international scales alongside global efforts to give reefs a chance.https://nsuworks.nova.edu/occ_icrs/1000/thumbnail.jp
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