hagis, an R Package Resource for Pathotype Analysis of Phytophthora sojae Populations Causing Stem and Root Rot of Soybean

Phytophthora sojae is a significant pathogen of soybean worldwide. Pathotype surveys for Phytophthora sojae are conducted to monitor resistance gene efficacy and determine if new resistance genes are needed. Valuable measurements for pathotype analysis include the distribution of susceptible reactions, pathotype complexity, pathotype frequency, and diversity indices for pathotype distributions. Previously the Habgood-Gilmour Spreadsheet (HaGiS), written in Microsoft Excel, was used for data analysis. However, the growing popularity of the R programming language in plant pathology and desire for reproducible research made HaGiS a prime candidate for conversion into an R package. Here we report on the development and use of an R package, hagis, that can be used to produce all outputs from the HaGiS Excel sheet for P. sojae or other gene-for-gene pathosystem studies

Will Jets Identify the Progenitors of Type Ia Supernovae?

We use the fact that a Type Ia supernova has been serendipitously discovered near the jet of the active galaxy 3C 78 to examine the question of whether jets can enhance accretion onto white dwarfs. One interesting outcome of such a jet-induced accretion process is an enhanced rate of novae in the vicinity of jets. We present results of observations of the jet in M87 which appear to have indeed discovered 11 novae in close proximity to the jet. We show that a confirmation of the relation between jets and novae and Type Ia supernovae can finally identify the elusive progenitors of Type Ia supernovae.Comment: 10 pages, 3 figure

ThetaProbe

ThetaProbe 1.0.3 (2017-11-8) Bug fixes Fix bugs where the complete CSV data file was not generated after fetching data from the server due to incorrect handling of only one probe loggin

Linear modelling of soil temperature effects on root lesion nematode population densities in R [Blog post]

Pratylenchus thornei, the root-lesion nematode, is widely distributed in wheat (Triticum aestivum) growing areas of many countries and is of particular concern in sub-tropical environments (Thompson 2015). These nematodes penetrate roots to feed and reproduce in the root cortex leading to loss of root function, which affects nutrient and water uptake of nutrients and water causing nutrient deficiency and water stress (Thompson 2015). In the original paper the population response of P. thornei in Queensland, Australia wheat to temperature is modelled using a linear and quadratic equations. The study aimed to investigate the effects of soil profile temperatures after different sowing dates on reproduction of the nematodes in susceptible and moderately resistant wheat cultivars in the subtropical grain region of eastern Australia. This document recreates the models for population densities of P. thornei as described in the original paper

Can rainfall be a useful predictor of epidemic risk across temporal and spatial scales?

Plant disease epidemics are largely driven by within-season weather variables when inoculum is not limiting. Commonly, predictors in risk assessment models are based on the interaction of temperature and wetness-related variables, relationships which are determined experimentally. There is an increasing interest in providing within-season or inter-seasonal risk information at the region or continent scale, which commonly use models developed for a smaller scale. Hence, the scale matching dilemma that challenges epidemiologists and meteorologists: upscale models or downscale weather data? Successful applications may be found in both cases, which should be supported by validation datasets whenever possible, to prove the usefulness of the approach. For some diseases, rainfall is key for inoculum dispersal and, in warmer regions (e.g., tropics) where temperature is less limiting for epidemics, rainfall extends wetness periods. The drawbacks of using rainfall at small scales relate to its discrete nature and high spatial variability. However, for pre- or early-season predictions at large spatial scales sources of reasonably accurate rainfall summaries are available and may prove useful. The availability of disease datasets at various scales allows the development and evaluation of new models to be applied at the correct scale. We will showcase examples and discuss the usefulness of rainfall as key variable to predict soybean rust and wheat scab from field to region