Natural selection in a postglacial range expansion: the case of the colour cline in the European barn owl.

Gradients of variation-or clines-have always intrigued biologists. Classically, they have been interpreted as the outcomes of antagonistic interactions between selection and gene flow. Alternatively, clines may also establish neutrally with isolation by distance (IBD) or secondary contact between previously isolated populations. The relative importance of natural selection and these two neutral processes in the establishment of clinal variation can be tested by comparing genetic differentiation at neutral genetic markers and at the studied trait. A third neutral process, surfing of a newly arisen mutation during the colonization of a new habitat, is more difficult to test. Here, we designed a spatially explicit approximate Bayesian computation (ABC) simulation framework to evaluate whether the strong cline in the genetically based reddish coloration observed in the European barn owl (Tyto alba) arose as a by-product of a range expansion or whether selection has to be invoked to explain this colour cline, for which we have previously ruled out the actions of IBD or secondary contact. Using ABC simulations and genetic data on 390 individuals from 20 locations genotyped at 22 microsatellites loci, we first determined how barn owls colonized Europe after the last glaciation. Using these results in new simulations on the evolution of the colour phenotype, and assuming various genetic architectures for the colour trait, we demonstrate that the observed colour cline cannot be due to the surfing of a neutral mutation. Taking advantage of spatially explicit ABC, which proved to be a powerful method to disentangle the respective roles of selection and drift in range expansions, we conclude that the formation of the colour cline observed in the barn owl must be due to natural selection.

Exchange bias between magnetoelectric YMnO3 and ferromagnetic SrRuO3 epitaxial films

Orthorhombic YMnO3 (YMO) epitaxial thin films were deposited on SrTiO3 (STO) single-crystal substrates. We show that the out-of-plane texture of the YMO films can be tailored using STO substrates having (001), (110), or (111) orientations. We report on the magnetic properties of the YMO(010) films grown on STO(001) substrates. The dependence of the susceptibility on the temperature indicates that the films are antiferromagnetic below the Néel temperature (around 35 K). Orthorhombic YMO(010) films were also deposited on an epitaxial buffer layer of ferromagnetic and metallic SrRuO3 (SRO). The magnetic hysteresis loops of SRO show exchange bias at temperatures below the Néel temperature of YMO. These results confirm that the YMO films are antiferromagnetic and demonstrate that magnetoelectric YMO can be integrated in functional epitaxial architectures.

Strategic Enterprise Architecture Management - Challenges, Best Practices, and Future Developments

The discipline of Enterprise Architecture Management (EAM) deals with the alignment of business and information systems architectures. While EAM has long been regarded as a discipline for IT managers this book takes a different stance: It explains how top executives can use EAM for leveraging their strategic planning and controlling processes and how EAM can contribute to sustainable competitive advantage. Based on the analysis of best practices from eight leading European companies from various industries the book presents crucial elements of successful EAM. It outlines what executives need to do in terms of governance, processes, methodologies and culture in order to bring their management to the next level. Beyond this, the book points how EAM might develop in the next decade allowing today's managers to prepare for the future of architecture management.

Velocity-jump instabilities in Hele-Shaw flow of associating polymer solutions

We study fracturelike flow instabilities that arise when water is injected into a Hele-Shaw cell filled with aqueous solutions of associating polymers. We explore various polymer architectures, molecular weights, and solution concentrations. Simultaneous measurements of the finger tip velocity and of the pressure at the injection point allow us to describe the dynamics of the finger in terms of the finger mobility, which relates the velocity to the pressure gradient. The flow discontinuities, characterized by jumps in the finger tip velocity, which are observed in experiments with some of the polymer solutions, can be modeled by using a nonmonotonic dependence between a characteristic shear stress and the shear rate at the tip of the finger. A simple model, which is based on a viscosity function containing both a Newtonian and a non-Newtonian component, and which predicts nonmonotonic regions when the non-Newtonian component of the viscosity dominates, is shown to agree with the experimental data.

The Role of Magnesium in Concrete Deterioration, HR-355, 1994

Concretes with service lives of less than 15 years and those with lives greater than 40 years were studied with petrographic microscope, scanning electron microscope, and electron microprobe to determine why these two groups of concrete exhibit such different degrees of durability under highway conditions. Coarse aggregate used in both types of concrete were from dolomite rock, but investigation revealed that dolomite aggregate in the two groups of concretes were much different in several respects. The poorly-performing aggregate is fine-grained, has numerous euhedral and subhedral dolomite rhombohedra, and has relatively high porosity. Aggregate from durable concrete is coarse-grained, with tightly interlocked crystal fabric, anhedral dolomite boundaries, and low porosity. Aggregate in short service life concrete was found to have undergone pervasive chemical reactions with the cement which produced reaction rims on the boundaries of coarse aggregate particles and in the cement region adjacent to aggregate boundaries. Textural and porosity differences are believed to be chiefly responsible for different service lives of the two groups of concrete. The basic reaction that has occurred in the short service life concretes between coarse aggregate and cement is an alkali-dolomite reaction. In the reaction dolomite from the aggregate reacts with hydroxide ions from the cement to free magnesium ions and carbonate ions, and the magnesium ions precipitate as brucite, Mg(OH)2. Simultaneously with this reaction, a second reaction occurs in which product carbonate ions react with portlandite from the cement to form calcite and hydroxide ions. Crystal growth pressures of newly formed brucite and calcite together with other processes, e.g. hydration state changes of magnesium chloride hydrates, lead to expansion of the concretes with resultant rapid deterioration. According to this model, magnesium from any source, either from reacting dolomite or from magnesium road deicers, has a major role in highway concrete deterioration. Consequently, magnesium deicers should be used with caution, and long-term testing of the effects of magnesium deicers on highway concrete should be implemented to determine their effects on durability.

Deep learning via semi-supervised embedding

We show how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be applied to deep multilayer architectures, either as a regularizer at the output layer, or on each layer of the architecture. This provides a simple alternative to existing approaches to deep learning whilst yielding competitive error rates compared to those methods, and existing shallow semi-supervised techniques.

Constraining ACT-R models of decision strategies: An experimental paradigm

It has been repeatedly debated which strategies people rely on in inference. These debates have been difficult to resolve, partially because hypotheses about the decision processes assumed by these strategies have typically been formulated qualitatively, making it hard to test precise quantitative predictions about response times and other behavioral data. One way to increase the precision of strategies is to implement them in cognitive architectures such as ACT-R. Often, however, a given strategy can be implemented in several ways, with each implementation yielding different behavioral predictions. We present and report a study with an experimental paradigm that can help to identify the correct implementations of classic compensatory and non-compensatory strategies such as the take-the-best and tallying heuristics, and the weighted-linear model.

Performance of Randomly Oriented, Fiber-Reinforced Roadway Soils, HR-211, 1982

Several primary techniques have been developed through which soil aggregate road material properties may be improved. Such techniques basically involve a mechanism of creating a continuous matrix system of soil and/or aggregate particles, interlocked through the use of some additive such as portland cement, lime, or bituminous products. Details by which soils are stabilized vary greatly, but they are dependent on the type of stabilizing agent and nature of the soil, though the overall approach to stabilization has the common feature that improvement is achieved by some mechanism(s) forcing individual particles to adhere to one another. This process creates a more rigid material, most often capable of resisting the influx of water during freezing, loss of strength due to high moisture content and particle dispersion during thawing, and loss of strength due to migration of fines and/or water by capillarity and pumping. The study reported herein, took a new and relatively different approach to strengthening of soils, i.e., improvement of roadway soils and/or soil-aggregate materials by structural reinforcement with randomly oriented fibers. The purpose of the study was to conduct a laboratory and field investigation into the potential of improving (a) soil-aggregate surfaced and subgrade materials, including those that are frost-prone and/or highly moisture susceptible, and (b) localized base course materials, by uniting such materials through fibrous reinforcement. The envisioned objective of the project was the development of a simple construction technique(s) that could be (a) applied on a selective basis to specific areas having a history of poor performance, or (b) used for improvement of potential base materials prior to surfacing. Little background information on such purpose and objective was available. Though the envisioned process had similarities to fibrous reinforced concrete, and to fibrous reinforced resin composites, the process was devoid of a cementitious binder matrix and thus highly dependent on the cohesive and frictional interlocking processes of a soil and/or aggregate with the fibrous reinforcement; a condition not unlike the introduction of reinforcing bars into a concrete sand/aggregate mixture without benefit of portland cement. Thus the study was also directed to answering some fundamental questions: (1) would the technique work; (2) what type or types of fibers are effective; (3) are workable fibers commercially available; and (4) can such fibers be effectively incorporated with conventional construction equipment, and employed in practical field applications? The approach to obtaining answers to these questions, was guided by the philosophy that an understanding of basic fundamentals was essential to developing a body of engineering knowledge, that would serve as the basis for eventual development of design procedures with fibrous products for the applications previously noted.

Positional bias of general and tissue-specific regulatory motifs in mouse gene promoters

Background: The arrangement of regulatory motifs in gene promoters, or promoterarchitecture, is the result of mutation and selection processes that have operated over manymillions of years. In mammals, tissue-specific transcriptional regulation is related to the presence ofspecific protein-interacting DNA motifs in gene promoters. However, little is known about therelative location and spacing of these motifs. To fill this gap, we have performed a systematic searchfor motifs that show significant bias at specific promoter locations in a large collection ofhousekeeping and tissue-specific genes.Results: We observe that promoters driving housekeeping gene expression are enriched inparticular motifs with strong positional bias, such as YY1, which are of little relevance in promotersdriving tissue-specific expression. We also identify a large number of motifs that show positionalbias in genes expressed in a highly tissue-specific manner. They include well-known tissue-specificmotifs, such as HNF1 and HNF4 motifs in liver, kidney and small intestine, or RFX motifs in testis,as well as many potentially novel regulatory motifs. Based on this analysis, we provide predictionsfor 559 tissue-specific motifs in mouse gene promoters.Conclusion: The study shows that motif positional bias is an important feature of mammalianproximal promoters and that it affects both general and tissue-specific motifs. Motif positionalconstraints define very distinct promoter architectures depending on breadth of expression andtype of tissue.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

quantiNemo: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation.

quantiNemo is an individual-based, genetically explicit stochastic simulation program. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits with varying architectures in structured populations connected by migration and located in a heterogeneous habitat. quantiNemo is highly flexible at various levels: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography, mating system, etc. quantiNemo is coded in C++ using an object-oriented approach and runs on any computer platform. Availability: Executables for several platforms, user's manual, and source code are freely available under the GNU General Public License at http://www2.unil.ch/popgen/softwares/quantinemo.

Deciphering the global organization of clustering in real complex networks

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Supergenes and complex phenotypes.

Understanding the molecular underpinnings of evolutionary adaptations is a central focus of modern evolutionary biology. Recent studies have uncovered a panoply of complex phenotypes, including locally adapted ecotypes and cryptic morphs, divergent social behaviours in birds and insects, as well as alternative metabolic pathways in plants and fungi, that are regulated by clusters of tightly linked loci. These 'supergenes' segregate as stable polymorphisms within or between natural populations and influence ecologically relevant traits. Some supergenes may span entire chromosomes, because selection for reduced recombination between a supergene and a nearby locus providing additional benefits can lead to locus expansions with dynamics similar to those known for sex chromosomes. In addition to allowing for the co-segregation of adaptive variation within species, supergenes may facilitate the spread of complex phenotypes across species boundaries. Application of new genomic methods is likely to lead to the discovery of many additional supergenes in a broad range of organisms and reveal similar genetic architectures for convergently evolved phenotypes.