Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology.

BACKGROUND: The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years. SCOPE: Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants. CONCLUSIONS: As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.

Evolution of specialization: a phylogenetic study of host range in the red milkweed beetle (Tetraopes tetraophthalmus).

Specialization is common in most lineages of insect herbivores, one of the most diverse groups of organisms on earth. To address how and why specialization is maintained over evolutionary time, we hypothesized that plant defense and other ecological attributes of potential host plants would predict the performance of a specialist root-feeding herbivore (the red milkweed beetle, Tetraopes tetraophthalmus). Using a comparative phylogenetic and functional trait approach, we assessed the determinants of insect host range across 18 species of Asclepias. Larval survivorship decreased with increasing phylogenetic distance from the true host, Asclepias syriaca, suggesting that adaptation to plant traits drives specialization. Among several root traits measured, only cardenolides (toxic defense chemicals) correlated with larval survival, and cardenolides also explained the phylogenetic distance effect in phylogenetically controlled multiple regression analyses. Additionally, milkweed species having a known association with other Tetraopes beetles were better hosts than species lacking Tetraopes herbivores, and milkweeds with specific leaf area values (a trait related to leaf function and habitat affiliation) similar to those of A. syriaca were better hosts than species having divergent values. We thus conclude that phylogenetic distance is an integrated measure of phenotypic and ecological attributes of Asclepias species, especially defensive cardenolides, which can be used to explain specialization and constraints on host shifts over evolutionary time.

Childhood epilepsy with neuropsychological regression and continuous spike waves during sleep: epilepsy surgery in a young adult.

We describe the case of a man with a history of complex partial seizures and severe language, cognitive and behavioural regression during early childhood (3.5 years), who underwent epilepsy surgery at the age of 25 years. His early epilepsy had clinical and electroencephalogram features of the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia (Landau-Kleffner syndrome), which we considered initially to be of idiopathic origin. Seizures recurred at 19 years and presurgical investigations at 25 years showed a lateral frontal epileptic focus with spread to Broca's area and the frontal orbital regions. Histopathology revealed a focal cortical dysplasia, not visible on magnetic resonance imaging. The prolonged but reversible early regression and the residual neuropsychological disorders during adulthood were probably the result of an active left frontal epilepsy, which interfered with language and behaviour during development. Our findings raise the question of the role of focal cortical dysplasia as an aetiology in the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia.

Evolutionary ecology of the colour-dimorphic rewardless orchid "Dactylorhiza sambucina"

Abstract Phenotypic polymorphism is an ideal system to study natural selection in wild populations, because it allows tracking population genetic changes by means of phenotypic changes. A wide variety of polymorphic traits have been studied in numerous animals and plants, as for example colour patterns in moths, snails and birds, human laterality, male reproductive strategies, plant morphology or mating systems. This thesis focused on Dactylorhiza sarnbucina, a rewardless European orchid species, showing a striking flower colour polymorphism, with either yellow or red flowered individuals co-occurring in natural populations. Several studies have investigated its evolutionary ecology since Nilsson's seminal paper in 1980, with a particular emphasis in the evolution and maintenance of its colour polymorphism. One of the main selective forces proposed to maintain this colour polymorphism was pollinator driven negative frequency-dependent selection (NFDS), when each morph is advantaged when rare, and comparatively disadvantaged when common. However, other investigators have recently questioned the occurrence of NFDS, and proposed alternatively that fluctuating selection may maintain this colour polymorphism. In this thesis, we aimed at reviewing and synthesizing these different studies, and also brought our contribution on D. sambucina reproductive ecology. Because numerous hypotheses have still to be tested, we concluded by saying that we are a long way from understanding the evolution and dynamics of colour polymorphism in natural D. sambucina populations. Beside the debated question of colour polymorphism maintenance, one question remained to be tested: what are the consequences of polymorphism per se. We experimentally addressed this question using artificial populations of D. sambucina, and found no relationship between population phenotypic diversity and orchid pollination success. This finding suggest that polymorphism itself was not an advantage for deceptive species such D sambucina, contrarily to the expectations. Finally, we suggest potential research perspectives that could allow a better understanding of the evolutionary ecology of this species. Résumé Le polymorphisme phénotypique est un système biologique idéal pour étudier l'action de la sélection en populations naturelles, grâce à la possibilité de suivre les changements génétiques de la population en étudiant les phénotypes des individus. De très nombreuses études ont montré du polymorphisme phénotypique chez les animaux, par exemple la latéralité chez l'Homme, la coloration des escargots ou des oiseaux. Dans le règne végétal, le polymorphisme est souvent associé à des traits du système de reproduction. Cette thèse est centrée sur une espèce d'orchidée Européenne qui ne produit pas de nectar, Dactylorhiza sambucina. Cette espèce présente des individus à fleurs jaunes et des individus à fleurs rouge, généralement présents en mélange dans les populations naturelles. Plusieurs études ont investigué l'écologie évolutive de cette espèce depuis 25 ans, avec comme thème central l'évolution et le maintien de ce polymorphisme. La principale force sélective proposée pour maintenir ce polymorphisme de couleur est la sélection fréquence-dépendante, exercée par le comportement des pollinisateurs. Chacun des deux variants de couleur est favorisé quand il est rare, et défavorisé quand il devient commun. Bien que ce mécanisme semble agir, certains auteurs doutent de son importance, et ont proposé que les variations temporelles ou spatiales des forces de sélection puisse maintenir le polymorphisme de couleur chez D. sambucina. Dans cette thèse, nous avons voulu résumer et synthétiser les résultats de ces différentes études, et aussi présenter des données nouvelles concernant la reproduction de cette espèce. À la vue de ces résultats, il apparait que de nombreux points nécessitent des expériences complémentaires, et que la compréhension de ce système biologique est encore fragmentaire. Nous nous sommes également intéressés à une question laissée en suspens dans la littérature: le polymorphisme de couleur en soit confère-t-il un avantage à l'espèce, comme proposé par certains auteurs? En construisant des populations artificielles de D. sambucina, nous avons pu montrer que le polymorphisme de couleur n'augmente pas le succès reproducteur de l'espèce. Nous terminons ce travail de recherche en proposant plusieurs axes de recherche pouvant conduire à une meilleure compréhension de l'écologie et de l'évolution de cette espèce.

Molecular dating, evolutionary rates, and the age of the grasses.

Many questions in evolutionary biology require an estimate of divergence times but, for groups with a sparse fossil record, such estimates rely heavily on molecular dating methods. The accuracy of these methods depends on both an adequate underlying model and the appropriate implementation of fossil evidence as calibration points. We explore the effect of these in Poaceae (grasses), a diverse plant lineage with a very limited fossil record, focusing particularly on dating the early divergences in the group. We show that molecular dating based on a data set of plastid markers is strongly dependent on the model assumptions. In particular, an acceleration of evolutionary rates at the base of Poaceae followed by a deceleration in the descendants strongly biases methods that assume an autocorrelation of rates. This problem can be circumvented by using markers that have lower rate variation, and we show that phylogenetic markers extracted from complete nuclear genomes can be a useful complement to the more commonly used plastid markers. However, estimates of divergence times remain strongly affected by different implementations of fossil calibration points. Analyses calibrated with only macrofossils lead to estimates for the age of core Poaceae ∼51-55 Ma, but the inclusion of microfossil evidence pushes this age to 74-82 Ma and leads to lower estimated evolutionary rates in grasses. These results emphasize the importance of considering markers from multiple genomes and alternative fossil placements when addressing evolutionary issues that depend on ages estimated for important groups.

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.

J-shaped association between serum glucose and functional outcome in acute ischemic stroke.

BACKGROUND AND PURPOSE: Hyperglycemia after stroke is associated with larger infarct volume and poorer functional outcome. In an animal stroke model, the association between serum glucose and infarct volume is described by a U-shaped curve with a nadir ≈7 mmol/L. However, a similar curve in human studies was never reported. The objective of the present study is to investigate the association between serum glucose levels and functional outcome in patients with acute ischemic stroke. METHODS: We analyzed 1446 consecutive patients with acute ischemic stroke. Serum glucose was measured on admission at the emergency department together with multiple other metabolic, clinical, and radiological parameters. National Institutes of Health Stroke Scale (NIHSS) score was recorded at 24 hours, and Rankin score was recorded at 3 and 12 months. The association between serum glucose and favorable outcome (Rankin score ≤2) was explored in univariate and multivariate analysis. The model was further analyzed in a robust regression model based on fractional polynomial (-2-2) functions. RESULTS: Serum glucose is independently correlated with functional outcome at 12 months (OR, 1.15; P=0.01). Other predictors of outcome include admission NIHSS score (OR, 1.18; P<0001), age (OR, 1.06; P<0.001), prestroke Rankin score (OR, 20.8; P=0.004), and leukoaraiosis (OR, 2.21; P=0.016). Using these factors in multiple logistic regression analysis, the area under the receiver-operator characteristic curve is 0.869. The association between serum glucose and Rankin score at 12 months is described by a J-shaped curve with a nadir of 5 mmol/L. Glucose values between 3.7 and 7.3 mmol/L are associated with favorable outcome. A similar curve was generated for the association of glucose and 24-hour NIHSS score, for which glucose values between 4.0 and 7.2 mmol/L are associated with a NIHSS score <7. Discussion-Both hypoglycemia and hyperglycemia are dangerous in acute ischemic stroke as shown by a J-shaped association between serum glucose and 24-hour and 12-month outcome. Initial serum glucose values between 3.7 and 7.3 mmol/L are associated with favorable outcome.

Evolutionary footprint of coevolving positions in genes.

MOTIVATION: The analysis of molecular coevolution provides information on the potential functional and structural implication of positions along DNA sequences, and several methods are available to identify coevolving positions using probabilistic or combinatorial approaches. The specific nucleotide or amino acid profile associated with the coevolution process is, however, not estimated, but only known profiles, such as the Watson-Crick constraint, are usually considered a priori in current measures of coevolution. RESULTS: Here, we propose a new probabilistic model, Coev, to identify coevolving positions and their associated profile in DNA sequences while incorporating the underlying phylogenetic relationships. The process of coevolution is modeled by a 16 × 16 instantaneous rate matrix that includes rates of transition as well as a profile of coevolution. We used simulated, empirical and illustrative data to evaluate our model and to compare it with a model of 'independent' evolution using Akaike Information Criterion. We showed that the Coev model is able to discriminate between coevolving and non-coevolving positions and provides better specificity and specificity than other available approaches. We further demonstrate that the identification of the profile of coevolution can shed new light on the process of dependent substitution during lineage evolution.

Evolutionary and dispersal history of Eurasian house mice Mus musculus clarified by more extensive geographic sampling of mitochondrial DNA.

We examined the sequence variation of mitochondrial DNA control region and cytochrome b gene of the house mouse (Mus musculus sensu lato) drawn from ca. 200 localities, with 286 new samples drawn primarily from previously unsampled portions of their Eurasian distribution and with the objective of further clarifying evolutionary episodes of this species before and after the onset of human-mediated long-distance dispersals. Phylogenetic analysis of the expanded data detected five equally distinct clades, with geographic ranges of northern Eurasia (musculus, MUS), India and Southeast Asia (castaneus, CAS), Nepal (unspecified, NEP), western Europe (domesticus, DOM) and Yemen (gentilulus). Our results confirm previous suggestions of Southwestern Asia as the likely place of origin of M. musculus and the region of Iran, Afghanistan, Pakistan, and northern India, specifically as the ancestral homeland of CAS. The divergence of the subspecies lineages and of internal sublineage differentiation within CAS were estimated to be 0.37-0.47 and 0.14-0.23 million years ago (mya), respectively, assuming a split of M. musculus and Mus spretus at 1.7 mya. Of the four CAS sublineages detected, only one extends to eastern parts of India, Southeast Asia, Indonesia, Philippines, South China, Northeast China, Primorye, Sakhalin and Japan, implying a dramatic range expansion of CAS out of its homeland during an evolutionary short time, perhaps associated with the spread of agricultural practices. Multiple and non-coincident eastward dispersal events of MUS sublineages to distant geographic areas, such as northern China, Russia and Korea, are inferred, with the possibility of several different routes.

Comment on 'The Molecular Evolutionary Patterns of the Insulin/FOXO Signaling Pathway'

Letter to the Editor on Wang M, Wang Q, Wang Z, Zhang X, Pan Y. The molecular evolutionary patterns of the insulin/FOXO signaling pathway

Performance of juveniles of Pseudoplatystoma fasciatum fed graded levels of corn gluten meal

The objective of this work was to evaluate corn gluten meal (CGM) as a substitute for fish meal in diets for striped catfish (Pseudoplatystoma fasciatum) juveniles. Eight isonitrogenous (46% crude protein) and isoenergetic (3,450 kcal kg-1 digestible energy) diets, with increasing levels of CGM - 0, 6, 12, 18, 24, 30, 36, and 42% -, were fed to juvenile striped catfish (113.56±5.10 g) for seven weeks. Maximum values for weight gain, specific growth rate, protein efficiency ratio and feed conversion ratio, evaluated by polynomial quadratic regression, were observed with 10.4, 11.4, 15.4 and 15% of CGM inclusion, respectively. Feed intake decreased significantly from 0.8% CGM. Mesenteric fat index and body gross energy decreased linearly with increasing levels of CGM; minimum body protein contents were observed with 34.1% CGM. Yellow pigmentation of fillets significantly increased until 26.5% CGM, and decreased from this point forth. Both plasma glucose and protein concentrations decreased with increased CGM levels. The inclusion of 10-15% CGM promotes optimum of striped catfish juveniles depending on the parameter evaluated. Yellow coloration in fillets produced by CGM diets can have marketing implications.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.