Stability of Perovskite (MgSiO3) in the Earth's Mantle

Available thermodynamic data and seismic models favor perovskite (MgSiO3) as the stable phase in the mantle. MgSiO3 was heated at temperatures from 1900 to 3200 kelvin with a Nd-YAG laser in diamond-anvil cells to study the phase relations at pressures from 45 to 100 gigapascals. The quenched products were studied with synchrotron x-ray radiation. The results show that MgSiO3 broke down to a mixture of MgO (periclase) and SiO2 (stishovite or an unquenchable polymorph) at pressures from 58 to 85 gigapascals. These results imply that perovskite may not be stable in the lower mantle and that it might be necessary to reconsider the compositional and density models of the mantle.     

Acceleration of uranium at the bevalac

Recent upgrading projects have extended the mass range of particles that can be accelerated at the Bevalac to include any element of the periodic table to energies above 1 billion electron volts per atomic mass unit. This capability was verified on 11 May 1982 with the production of a uranium beam at 147.7 million electron volts per atomic mass unit.     

Soil microbial activity is affected by Roundup WeatherMax and pesticides applied to cotton (Gossypium hirsutum)

Adoption of glyphosate-based weed control systems has led to increased use of the herbicide with continued use of additional pesticides. Combinations of pesticides may affect soil microbial activity differently than pesticides applied alone. Research was conducted to evaluate the influence of glyphosate-based cotton pest management systems on soil microbial activity. Soil was treated with commercial formulations of trifluralin, aldicarb, and mefenoxam + pentachloronitrobenzene (PCNB) with or without glyphosate (applied as Roundup WeatherMax). The soil microbial activity was measured by quantifying C and N mineralization. Soil microbial biomass was determined using the chloroform fumigation-incubation method. Soils treated with glyphosate alone exhibited greater cumulative C mineralization 30 days after treatment than all other treatments, which were similar to the untreated control. The addition of Roundup WeatherMax reduced C mineralization in soils treated with fluometuron, aldicarb, or mefenoxam + PCNB formulations. These results indicate that glyphosate-based herbicides alter the soil microbial response to other pesticides.     

Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning

Hundreds of species in one of Australia's dominant plant families, the Myrtaceae, are at risk from the invasive pathogenic fungus Austropuccinia psidii. Since its arrival in Australia in 2010, native plant communities have been severely affected, with highly susceptible species likely to become extinct from recurring infections. While severe impact on Australian native and plantation forestry has been predicted, the lemon myrtle industry is already under threat. Commercial cultivars of lemon myrtle (Backhousia citriodora) are highly susceptible to A. psidii. Detecting and monitoring disease outbreaks is currently only possible by eye, which is costly and subject to human bias. This study aims at developing a proof‐of‐concept for automated, non‐biased classification of healthy (naïve), fungicide‐treated and diseased lemon myrtle trees by means of their spectral reflectance signatures. From a lemon myrtle plantation, spectral signatures of fungicide‐treated and untreated leaves were collected using a portable field spectrometer. A third class of spectra, from naïve lemon myrtle leaves that had not been exposed to A. psidii, was collected from a botanical garden. Reflectance spectra in their primary form and their first‐order derivatives were used to train a random forest classifier resulting in an overall accuracy of 78% (kappa = 0.68) for primary spectra and 95% (kappa = 0.92) for first‐order derivative‐transformed spectra. Thus, an optical sensor‐based discrimination, using spectral reflectance signatures of this as yet uninvestigated pathosystem, seems technically feasible. This study provides a foundation for the development of automated, sensor‐based detection and monitoring systems for myrtle rust.