Effects of Storage Conditions on Endophyte and Seed Viability in Pasture Grasses

Several important temperate pasture grasses have co-evolved with mutualistic Epichloë fungal endophytes. These endophytes impart beneficial attributes to their host as they enhance the fitness of the grass when under biotic and abiotic stresses. The asexual species of these fungi (formerly classed as Neotyphodium) are obligate symbionts, and efficiently colonise newly formed tillers and infect seed by direct colonisation of the embryo. These endophytes are strictly seed transmitted. Survival of the fungus in this seed is therefore critical for the dissemination of endophyte-infected seed to grassland farmers. Longevity of endophyte in stored seed is primarily determined by the length of storage, temperature, and relative humidity as this is in equilibrium with seed moisture. Elevated temperature and relative humidity both reduce endophyte viability. The relative importance of each of these environmental parameters is unclear. Longevity may be further modified by grass species, cultivar, seed lot, and endophyte strain. Valuable seed requiring long term storage can utilise controlled storage facilities where temperature is preferably ≤ 5oC and relative humidity ≤ 30% (seed moisture \u3c 8%). For large quantities of commercial seed, moisture barrier packaging can be used

Towards laser driven table-top coherent diffractive X-ray microscopy of cultured hippocampal neurons

Neurodegenerative diseases such as Alzheimer’s disease have a huge impact on the world population; over 44 million people worldwide and 850,000 in the UK were recorded as living with dementia in 2013. There are numerous theories attempting to explain the cause of Alzheimer’s disease. Histology from the brains of people who had Alzheimer’s disease shows neurofibilliary tangles and amyloid plaques. Their role in the mechanism of disease is not yet completely understood but we envisage that novel imaging techniques may aid understanding. We present initial data collected using confocal fluorescence microscopy and hard X-ray scanning diffractive microscopy (ptychography) on cultured neuron samples plus high resolution large field of view imaging of test samples from a soft X-ray lab based high harmonic generation (HHG) source

Sipha maydis sensitivity to defences of Lolium multiflorum and its endophytic fungus Epichloë occultans

Background. Plants possess a sophisticated immune system to defend from herbivores. These defence responses are regulated by plant hormones including salicylic acid (SA) and jasmonic acid (JA). Sometimes, plant defences can be complemented by the presence of symbiotic microorganisms. A remarkable example of this are grasses establishing symbiotic associations with Epichloë fungal endophytes. We studied the level of resistance provided by the grass’ defence hormones, and that provided by Epichloë fungal endophytes, against an introduced herbivore aphid. These fungi protect their hosts against herbivores by producing bioactive alkaloids. We hypothesized that either the presence of fungal endophytes or the induction of the plant salicylic acid (SA) defence pathway would enhance the level of resistance of the grass to the aphid. Methods. Lolium multiflorum plants, with and without the fungal endophyte Epichloë occultans, were subjected to an exogenous application of SA followed by a challenge with the aphid, Sipha maydis. Results. Our results indicate that neither the presence of E. occultans nor the induction of the plant’s SA pathway regulate S. maydis populations. However, endophytesymbiotic plants may have been more tolerant to the aphid feeding because these plants produced more aboveground biomass. We suggest that this insect insensitivity could be explained by a combination between the ineffectiveness of the specific alkaloids produced by E. occultans in controlling S. maydis aphids and the capacity of this herbivore to deal with hormone-dependent defences of L. multiflorum.Fil: Bastias, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Grasslands Research Centre; Nueva ZelandaFil: Martinez-Ghersa, Maria Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Newman, Jonathan A.. Wilfrid Laurier University; CanadáFil: Card, Stuart D.. Grasslands Research Centre; Nueva ZelandaFil: Mace, Wade J.. Grasslands Research Centre; Nueva ZelandaFil: Gundel, Pedro Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentin

Jasmonic acid regulation of the anti-herbivory mechanism conferred by fungal endophytes in grasses

The most studied mechanism of protection against herbivores in grasses associated with Epichloë fungal endophytes has been the fungal production of alkaloids. However, the contribution of the plant immune response on the level of resistance to herbivores in symbiotic grasses has been poorly explored. We studied the relationship between the plant hormone, jasmonic acid (JA) and Epichloë fungal endophytes on herbivore defences in symbiotic grasses. We hypothesized that an exogenous application of methyl jasmonate (MeJA), an activator of the plant JA defence response, would increase the level of resistance of endophyte-symbiotic and non-symbiotic plants to a chewing insect. As Epichloë endophytes produce alkaloids, an enhancement of the JA defence would complement the resistance given by these alkaloids. Lolium multiflorum plants symbiotic and non-symbiotic with the endophyte Epichloë occultans were subjected to an exogenous application of MeJA followed by a challenge with the generalist chewing insect Spodoptera frugiperda. We measured the level of plant resistance to chewing insects, and the defences conferred by host plants and fungal endophytes. Symbiotic plants were more resistant to S. frugiperda than their non-symbiotic counterparts. However, despite the fact that the concentration of JA significantly increased in all plants exposure to MeJA, neither endophyte-symbiotic nor non-symbiotic plants showed an enhanced resistance to insects. Unexpectedly, the exposure of endophyte-symbiotic plants to MeJA led to a reduction in the concentration of loline alkaloids (i.e. N-formyllolines and N-acetylnorlolines), consequently decreasing the level of plant resistance to the herbivore. Synthesis. Our results suggest that, rather than complementing the alkaloid-based defence, the jasmonic acid hormone weakens the anti-herbivore mechanism conferred by Epichloë endophytes. The present study highlights that the interaction between the jasmonic acid hormone and the presence of leaf fungal endophytes can be of importance for the effectiveness of the anti-herbivore defences of symbiotic plants.Fil: Bastias Campos, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Martinez-Ghersa, Maria Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Newman, Jonathan A.. University of Guelph; CanadáFil: Card, Stuart D.. AgResearch; Nueva ZelandaFil: Mace, Wade J.. AgResearch; Nueva ZelandaFil: Gundel, Pedro Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentin

Overtime working, the Phillips curve and the wage curve: British engineering, 1926-66

This paper shows that wage-unemployment elasticities derived from estimated wage curves and Phillips curves may be critically dependent on the measurement of wages. Incorporating hourly wage earnings, that include the influence of overtime payments, can lead to seriously distorted results. Meaningful elasticities are obtained only if hourly standard wages form the basis of analysis. Work is based on a unique data set describing two homogeneous blue-collar occupational groups - skilled fitters and unskilled labourers - in the British engineering industry. Each group is also divided into timeworkers and piece-rate workers. Data are aggregated into a panel of 28 local labour markets and cover the highly contrasting periods, 1928-1938 and 1954-1966

Disruption prediction at JET through deep convolutional neural networks using spatiotemporal information from plasma profiles

In view of the future high power nuclear fusion experiments, the early identification of disruptions is a mandatory requirement, and presently the main goal is moving from the disruption mitigation to disruption avoidance and control. In this work, a deep-convolutional neural network (CNN) is proposed to provide early detection of disruptive events at JET. The CNN ability to learn relevant features, avoiding hand-engineered feature extraction, has been exploited to extract the spatiotemporal information from 1D plasma profiles. The model is trained with regularly terminated discharges and automatically selected disruptive phase of disruptions, coming from the recent ITER-like-wall experiments. The prediction performance is evaluated using a set of discharges representative of different operating scenarios, and an in-depth analysis is made to evaluate the performance evolution with respect to the considered experimental conditions. Finally, as real-time triggers and termination schemes are being developed at JET, the proposed model has been tested on a set of recent experiments dedicated to plasma termination for disruption avoidance and mitigation. The CNN model demonstrates very high performance, and the exploitation of 1D plasma profiles as model input allows us to understand the underlying physical phenomena behind the predictor decision

A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices

Shattered pellet injection experiments at JET in support of the ITER disruption mitigation system design

A series of experiments have been executed at JET to assess the efficacy of the newly installed shattered pellet injection (SPI) system in mitigating the effects of disruptions. Issues, important for the ITER disruption mitigation system, such as thermal load mitigation, avoidance of runaway electron (RE) formation, radiation asymmetries during thermal quench mitigation, electromagnetic load control and RE energy dissipation have been addressed over a large parameter range. The efficiency of the mitigation has been examined for the various SPI injection strategies. The paper summarises the results from these JET SPI experiments and discusses their implications for the ITER disruption mitigation scheme

Performance Comparison of Machine Learning Disruption Predictors at JET

Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs