Association mapping of spot blotch resistance in wild barley

Spot blotch, caused by Cochliobolus sativus, is an important foliar disease of barley. The disease has been controlled for over 40 years through the deployment of cultivars with durable resistance derived from the line NDB112. Pathotypes of C. sativus with virulence for the NDB112 resistance have been detected in Canada; thus, many commercial cultivars are vulnerable to spot blotch epidemics. To increase the diversity of spot blotch resistance in cultivated barley, we evaluated 318 diverse wild barley accessions comprising the Wild Barley Diversity Collection (WBDC) for reaction to C. sativus at the seedling stage and utilized an association mapping (AM) approach to identify and map resistance loci. A high frequency of resistance was found in the WBDC as 95% (302/318) of the accessions exhibited low infection responses. The WBDC was genotyped with 558 Diversity Array Technology (DArT®) and 2,878 single nucleotide polymorphism (SNP) markers and subjected to structure analysis before running the AM procedure. Thirteen QTL for spot blotch resistance were identified with DArT and SNP markers. These QTL were found on chromosomes 1H, 2H, 3H, 5H, and 7H and explained from 2.3 to 3.9% of the phenotypic variance. Nearly half of the identified QTL mapped to chromosome bins where spot blotch resistance loci were previously reported, offering some validation for the AM approach. The other QTL mapped to unique genomic regions and may represent new spot blotch resistance loci. This study demonstrates that AM is an effective technique for identifying and mapping QTL for disease resistance in a wild crop progenitor

Citizen crowds and experts: observer variability in image-based plant phenotyping

Background:Image-based plant phenotyping has become a powerful tool in unravelling genotype–environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experiments. Yet we rely on observer (a human expert) input to perform the phenotyping process. We assume such input to be a ‘gold-standard’ and use it to evaluate software and algorithms and to train learning-based algorithms. However, we should consider whether any variability among experienced and non-experienced (including plain citizens) observers exists. Here we design a study that measures such variability in an annotation task of an integer-quantifiable phenotype: the leaf count.Results:We compare several experienced and non-experienced observers in annotating leaf counts in images of Arabidopsis Thaliana to measure intra- and inter-observer variability in a controlled study using specially designed annotation tools but also citizens using a distributed citizen-powered web-based platform. In the controlled study observers counted leaves by looking at top-view images, which were taken with low and high resolution optics. We assessed whether the utilization of tools specifically designed for this task can help to reduce such variability. We found that the presence of tools helps to reduce intra-observer variability, and that although intra- and inter-observer variability is present it does not have any effect on longitudinal leaf count trend statistical assessments. We compared the variability of citizen provided annotations (from the web-based platform) and found that plain citizens can provide statistically accurate leaf counts. We also compared a recent machine-learning based leaf counting algorithm and found that while close in performance it is still not within inter-observer variability.Conclusions:While expertise of the observer plays a role, if sufficient statistical power is present, a collection of non-experienced users and even citizens can be included in image-based phenotyping annotation tasks as long they are suitably designed. We hope with these findings that we can re-evaluate the expectations that we have from automated algorithms: as long as they perform within observer variability they can be considered a suitable alternative. In addition, we hope to invigorate an interest in introducing suitably designed tasks on citizen powered platforms not only to obtain useful information (for research) but to help engage the public in this societal important problem

Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Experiment Data User's Guide

The Stratospheric and Mesospheric Sounder (SAMS) aboard Nimbus-7 observes infrared radiation from the atmospheric limb. Global upper atmosphere temperature profiles and vertical concentrations of H2O, NO, N2O, CH4 and CO2 are derived from these measurements. The status of all channels was carefully monitored. Temperature and composition were retrieved from the measurements by linearizing the direct equation about an a priori profile and using an optimum statistical estimator to find the most likely solution. The derived temperature and composition profiles are archived on two tape products whose file structure and record formats are described in detail. The gridded retrieved temperature tape (GRID-T) contains daily day and night average temperatures at 62 pressure levels in a 2.5 degree latitude by 10 degree longitude grid extending from 67.5 degrees N to 50 degrees S. The zonal mean methane and nitrous oxide composition tape (ZMT-G) contains zonal mean day and night average CH4 and N2O mixing ratios at 31 pressure levels for 2.5 degrees latitude zones extending from 67.5 degrees N to 50 degrees S