Arbuscular mycorrhizal fungi reduce the differences in competitiveness between dominant and subordinate plant species

In grassland communities, plants can be classified as dominants or subordinates according to their relative abundances, but the factors controlling such distributions remain unclear. Here, we test whether the presence of the arbuscular mycorrhizal (AM) fungus Glomus intraradices affects the competitiveness of two dominant (Taraxacum officinale and Agrostis capillaris) and two subordinate species (Prunella vulgaris and Achillea millefolium). Plants were grown in pots in the presence or absence of the fungus, in monoculture and in mixtures of both species groups with two and four species. In the absence of G. intraradices, dominants were clearly more competitive than subordinates. In inoculated pots, the fungus acted towards the parasitic end of the mutualism-parasitism continuum and had an overall negative effect on the growth of the plant species. However, the negative effects of the AM fungus were more pronounced on dominant species reducing the differences in competitiveness between dominant and subordinate species. The effects of G. intraradices varied with species composition highlighting the importance of plant community to mediate the effects of AM fungi. Dominant species were negatively affected from the AM fungus in mixtures, while subordinates grew identically with and without the fungus. Therefore, our findings predict that the plant dominance hierarchy may flatten out when dominant species are more reduced than subordinate species in an unfavourable AM fungal relationship (parasitism

Eavesdropper's Optimal Information in Variations of Bennett-Brassard 1984 Quantum Key Distribution in the Coherent Attacks

We calculate eavesdropper's optimal information on raw bits in Bennett-Brassard 1984 quantum key distribution (BB84 QKD) and six-state scheme in coherent attacks, using a formula by Lo and Chau [Science 283 (1999) 2050] with single photon assumption. We find that eavesdropper's optimal information in QKD without public announcement of bases [Phys. Lett. A 244 (1998) 489] is the same as that of a corresponding QKD WITH it in the coherent attack. We observe a sum-rule concerning each party's information.Comment: no correction, 7 pages, 1 figure, RevTe

Arbuscular mycorrhizal fungi reduce the differences in competitiveness between dominant and subordinate plant species

In grassland communities, plants can be classified as dominants or subordinates according to their relative abundances, but the factors controlling such distributions remain unclear. Here, we test whether the presence of the arbuscular mycorrhizal (AM) fungus Glomus intraradices affects the competitiveness of two dominant (Taraxacum officinale and Agrostis capillaris) and two subordinate species (Prunella vulgaris and Achillea millefolium). Plants were grown in pots in the presence or absence of the fungus, in monoculture and in mixtures of both species groups with two and four species. In the absence of G. intraradices, dominants were clearly more competitive than subordinates. In inoculated pots, the fungus acted towards the parasitic end of the mutualism-parasitism continuum and had an overall negative effect on the growth of the plant species. However, the negative effects of the AM fungus were more pronounced on dominant species reducing the differences in competitiveness between dominant and subordinate species. The effects of G. intraradices varied with species composition highlighting the importance of plant community to mediate the effects of AM fungi. Dominant species were negatively affected from the AM fungus in mixtures, while subordinates grew identically with and without the fungus. Therefore, our findings predict that the plant dominance hierarchy may flatten out when dominant species are more reduced than subordinate species in an unfavourable AM fungal relationship (parasitism)

Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science

This paper describes a machine learning and data science pipeline for structured information extraction from documents, implemented as a suite of open-source tools and extensions to existing tools. It centers around a methodology for extracting procedural information in the form of recipes, stepwise procedures for creating an artifact (in this case synthesizing a nanomaterial), from published scientific literature. From our overall goal of producing recipes from free text, we derive the technical objectives of a system consisting of pipeline stages: document acquisition and filtering, payload extraction, recipe step extraction as a relationship extraction task, recipe assembly, and presentation through an information retrieval interface with question answering (QA) functionality. This system meets computational information and knowledge management (CIKM) requirements of metadata-driven payload extraction, named entity extraction, and relationship extraction from text. Functional contributions described in this paper include semi-supervised machine learning methods for PDF filtering and payload extraction tasks, followed by structured extraction and data transformation tasks beginning with section extraction, recipe steps as information tuples, and finally assembled recipes. Measurable objective criteria for extraction quality include precision and recall of recipe steps, ordering constraints, and QA accuracy, precision, and recall. Results, key novel contributions, and significant open problems derived from this work center around the attribution of these holistic quality measures to specific machine learning and inference stages of the pipeline, each with their performance measures. The desired recipes contain identified preconditions, material inputs, and operations, and constitute the overall output generated by our computational information and knowledge management (CIKM) system.Comment: 15th International Conference on Document Analysis and Recognition Workshops (ICDARW 2019

Multifunctional P-Doped TiO 2

Multifunctional P-doped TiO2 thin films were synthesized by atmospheric pressure chemical vapor deposition (APCVD). This is the first example of P-doped TiO2 films with both P5+ and P3– states, with the relative proportion being determined by synthesis conditions. This technique to control the oxidation state of the impurities presents a new approach to achieve films with both self-cleaning and TCO properties. The origin of electrical conductivity in these materials was correlated to the incorporation of P5+ species, as suggested by Hall Effect probe measurements. The photocatalytic performance of the films was investigated using the model organic pollutant, stearic acid, with films containing predominately P3– states found to be vastly inferior photocatalysts compared to undoped TiO2 films. Transient absorption spectroscopy studies also showed that charge carrier concentrations increased by several orders of magnitude in films containing P5+ species only, whereas photogenerated carrier lifetimes—and thus photocatalytic activity—were severely reduced upon incorporation of P3– species. The results presented here provide important insights on the influence of dopant nature and location within a semiconductor structure. These new P-doped TiO2 films are a breakthrough in the development of multifunctional advanced materials with tuned properties for a wide range of applications

Large-vscale hydrogen production and storage technologies: Current status and future directions

This is an accepted manuscript of an article published by Elsevier in International Journal of Hydrogen Energy on 13/11/2020, available online: https://doi.org/10.1016/j.ijhydene.2020.10.110 The accepted version of the publication may differ from the final published version.Over the past years, hydrogen has been identified as the most promising carrier of clean energy. In a world that aims to replace fossil fuels to mitigate greenhouse emissions and address other environmental concerns, hydrogen generation technologies have become a main player in the energy mix. Since hydrogen is the main working medium in fuel cells and hydrogen-based energy storage systems, integrating these systems with other renewable energy systems is becoming very feasible. For example, the coupling of wind or solar systems hydrogen fuel cells as secondary energy sources is proven to enhance grid stability and secure the reliable energy supply for all times. The current demand for clean energy is unprecedented, and it seems that hydrogen can meet such demand only when produced and stored in large quantities. This paper presents an overview of the main hydrogen production and storage technologies, along with their challenges. They are presented to help identify technologies that have sufficient potential for large-scale energy applications that rely on hydrogen. Producing hydrogen from water and fossil fuels and storing it in underground formations are the best large-scale production and storage technologies. However, the local conditions of a specific region play a key role in determining the most suited production and storage methods, and there might be a need to combine multiple strategies together to allow a significant large-scale production and storage of hydrogen.Published versio

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Building blocks for composable web services

Ph.D.Committee Chair: Ling Li