Pollination success in a deceptive orchid is enhanced by co-occurring rewarding magnet plants

It has been debated whether pollination success in nonrewarding plants that flower in association with nectar-producing plants will be diminished by competition for pollinator visits or, alternatively, enhanced through increased local abundance of pollinators (the magnet species effect). We experimentally evaluated these effects using the nonrewarding bumblebee-pollinated orchid Anacamptis morio and associated nectar-producing plants at a site in Sweden. Pollination success (estimated as pollen receipt and pollen removal) in A. morio was significantly greater for individuals translocated to patches of nectar-producing plants (Geum rivale and Allium schoenoprasum) than for individuals placed outside (similar to20 m away) such patches. These results provide support for the existence of a facilitative magnet species effect in the interaction between certain nectar plants and A. morio. To determine the spatial scale of these interactions, we correlated the visitation rate to flowers of A. morio with the density of sympatric nectar plants in 1-m(2) and 100-m(2) plots centered around groups of translocated plants, and at the level of whole meadows (similar to0.5-2 ha). Visitation rate to flowers of A. morio was not correlated with the 1-m(2) patch density of G. rivale and A. schoenoprasum, but showed a significant positive relationship with density of these nectar plants in 100-m(2) plots. In addition, visitation to flowers of A. morio was strongly and positively related to the density of A. schoenoprasum at the level of the meadow. Choice experiments showed that bees foraging on the purple flowers of A. schoenoprasum (a particularly effective magnet species) visit the purple flowers of A. morio more readily (47.6% of choices) than bees foraging on the yellow flowers of Lotus corniculatus (17% of choices). Overall similarity in flower color and shape may increase the probability that a pollinator will temporarily shift from a nectar-producing "magnet" plant to a nonrewarding plant. We discuss the possibility of a mimicry continuum between those orchids that exploit instinctive food-seeking behavior of pollinators and those that show an adaptive resemblance to nectar-producing plants

Scaling Analysis of Magnetic Filed Tuned Phase Transitions in One-Dimensional Josephson Junction Arrays

We have studied experimentally the magnetic field-induced superconductor-insulator quantum phase transition in one-dimensional arrays of small Josephson junctions. The zero bias resistance was found to display a drastic change upon application of a small magnetic field; this result was analyzed in context of the superfluid-insulator transition in one dimension. A scaling analysis suggests a power law dependence of the correlation length instead of an exponential one. The dynamical exponents $z$ were determined to be close to 1, and the correlation length critical exponents were also found to be about 0.3 and 0.6 in the two groups of measured samples.Comment: 4 pages, 4 figure

A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.Acknowledgments. S.C. thanks the FCT for the Ph.D. Grant SFRH/BD/ 80925/2011. The authors thank the FCT Strategic Project of UID/BIO/04469/2013 unit, the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and the project “BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes”, REF. NORTE-07-0124-FEDER-000028 Co-funded by the Programa Operacional Regional do Norte (ON.2 - O Novo Norte), QREN, FEDER

On magnetospheric electron impact ionisation and dynamics in Titan's ram-side and polar ionosphere – a Cassini case study

We present data from the sixth Cassini flyby of Titan (T5), showing that the magnetosphere of Saturn strongly interacts with the moon's ionosphere and exo-ionosphere. A simple electron ionisation model provides a reasonable agreement with the altitude structure of the ionosphere. Furthermore, we suggest that the dense and cold exo-ionosphere (from the exobase at 1430 km and outward to several Titan radii from the surface) can be explained by magnetospheric forcing and other transport processes whereas exospheric ionisation by impacting low energy electrons seems to play a minor role

Climate drives rhizosphere microbiome variation and divergent selection between geographically distant Arabidopsis populations

Disentangling the contribution of climatic and edaphic factors to microbiome variation and local adaptation in plants requires an experimental approach to uncouple their effects and test for causality. We used microbial inocula, soil matrices and plant genotypes derived from two natural Arabidopsis thaliana populations in northern and southern Europe in an experiment conducted in climatic chambers mimicking seasonal changes in temperature, day length and light intensity of the home sites of the two genotypes. The southern A. thaliana genotype outperformed the northern genotype in the southern climate chamber, whereas the opposite was true in the northern climate chamber. Recipient soil matrix, but not microbial composition, affected plant fitness, and effects did not differ between genotypes. Differences between chambers significantly affected rhizosphere microbiome assembly, although these effects were small in comparison with the shifts induced by physicochemical differences between soil matrices. The results suggest that differences in seasonal changes in temperature, day length and light intensity between northern and southern Europe have strongly influenced adaptive differentiation between the two A. thaliana populations, whereas effects of differences in soil factors have been weak. By contrast, below-ground differences in soil characteristics were more important than differences in climate for rhizosphere microbiome differentiation

Combining dispersion modelling with synoptic patterns to understand the wind-borne transport into the UK of the bluetongue disease vector

Bluetongue, an economically important animal disease, can be spread over long distances by carriage of insect vectors (Culicoides biting midges) on the wind. The weather conditions which influence the midge’s flight are controlled by synoptic scale atmospheric circulations. A method is proposed that links wind-borne dispersion of the insects to synoptic circulation through the use of a dispersion model in combination with principal component analysis (PCA) and cluster analysis. We illustrate how to identify the main synoptic situations present during times of midge incursions into the UK from the European continent. A PCA was conducted on high-pass-filtered mean sea-level pressure data for a domain centred over north-west Europe from 2005 to 2007. A clustering algorithm applied to the PCA scores indicated the data should be divided into five classes for which averages were calculated, providing a classification of the main synoptic types present. Midge incursion events were found to mainly occur in two synoptic categories; 64.8% were associated with a pattern displaying a pressure gradient over the North Atlantic leading to moderate south-westerly flow over the UK and 17.9% of the events occurred when high pressure dominated the region leading to south-easterly or easterly winds. The winds indicated by the pressure maps generally compared well against observations from a surface station and analysis charts. This technique could be used to assess frequency and timings of incursions of virus into new areas on seasonal and decadal timescales, currently not possible with other dispersion or biological modelling methods

Fear expression is suppressed by tyrosine administration

Animal studies have demonstrated that catecholamines regulate several aspects of fear conditioning. In humans, however, pharmacological manipulations of the catecholaminergic system have been scarce, and their primary focus has been to interfering with catecholaminergic activity after fear acquisition or expression had taken place, using L-Dopa, primarily, as catecholaminergic precursor. Here, we sought to determine if putative increases in presynaptic dopamine and norepinephrine by tyrosine administered before conditioning could affect fear expression. Electrodermal activity (EDA) of 46 healthy participants (24 placebo, 22 tyrosine) was measured in a fear instructed task. Results showed that tyrosine abolished fear expression compared to placebo. Importantly, tyrosine did not affect EDA responses to the aversive stimulus (UCS) or alter participants' mood. Therefore, the effect of tyrosine on fear expression cannot be attributed to these factors. Taken together, these findings provide evidence that the catecholaminergic system influences fear expression in humans

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system

Vegetation Type and Decomposition Priming Mediate Brackish Marsh Carbon Accumulation Under Interacting Facets of Global Change

Coastal wetland carbon pools are globally important, but their response to interacting facets of global change remain unclear. Numerical models neglect species-specific vegetation responses to sea level rise (SLR) and elevated CO2 (eCO2) that are observed in field experiments, while field experiments cannot address the long-term feedbacks between flooding and soil growth that models show are important. Here, we present a novel numerical model of marsh carbon accumulation parameterized with empirical observations from a long-running eCO2 experiment in an organic rich, brackish marsh. Model results indicate that eCO2 and SLR interact synergistically to increase soil carbon burial, driven by shifts in plant community composition and soil volume expansion. However, newly parameterized interactions between plant biomass and decomposition (i.e. soil priming) reduce the impact of eCO2 on marsh survival, and by inference, the impact of eCO2 on soil carbon accumulation

Hypofibrinolysis in diabetes: a therapeutic target for the reduction of cardiovascular risk

An enhanced thrombotic environment and premature atherosclerosis are key factors for the increased cardiovascular risk in diabetes. The occlusive vascular thrombus, formed secondary to interactions between platelets and coagulation proteins, is composed of a skeleton of fibrin fibres with cellular elements embedded in this network. Diabetes is characterised by quantitative and qualitative changes in coagulation proteins, which collectively increase resistance to fibrinolysis, consequently augmenting thrombosis risk. Current long-term therapies to prevent arterial occlusion in diabetes are focussed on anti-platelet agents, a strategy that fails to address the contribution of coagulation proteins to the enhanced thrombotic milieu. Moreover, antiplatelet treatment is associated with bleeding complications, particularly with newer agents and more aggressive combination therapies, questioning the safety of this approach. Therefore, to safely control thrombosis risk in diabetes, an alternative approach is required with the fibrin network representing a credible therapeutic target. In the current review, we address diabetes-specific mechanistic pathways responsible for hypofibrinolysis including the role of clot structure, defects in the fibrinolytic system and increased incorporation of anti-fibrinolytic proteins into the clot. Future anti-thrombotic therapeutic options are discussed with special emphasis on the potential advantages of modulating incorporation of the anti-fibrinolytic proteins into fibrin networks. This latter approach carries theoretical advantages, including specificity for diabetes, ability to target a particular protein with a possible favourable risk of bleeding. The development of alternative treatment strategies to better control residual thrombosis risk in diabetes will help to reduce vascular events, which remain the main cause of mortality in this condition