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The choice of a suitable index of similarity is a very important and decisive point for determining true genetic dissimilarity between individuals, clustering, analyzing diversity within populations and studying relationship between populations, because different dissimilarity indices may yield contrary outcomes. We show that there are no acceptable universal approaches for assessing dissimilarity between individuals with molecular markers. Different measures are applicable to dominant and codominant DNA markers depending on ploidy of organisms. We show that the Dice (Nei and Li) coefficient is the suitable measure for haploid organisms with codominant markers and it can be applied directly to {0,1}-vectors representing banding profiles of individuals. None of the common measures, Dice, Jaccard, simple mismatch coefficient (or the squared Euclidean distance), is appropriate for diploids with codominant markers. By transforming multiallelic banding patterns at each locus into the corresponding homozygous or heterozygous states, a new measure of dissimilarity within loci was developed and expanded to assess dissimilarity between multilocus states of two individuals by averaging across all codominant loci tested. There is no rigorous well-founded solution in the case of dominant markers. The simple mismatch coefficient is the most suitable measure of dissimilarity between banding patterns of closely related haploid forms. For distantly related haploid individuals, the Jaccard dissimilarity is recommended. In general, no suitable method for measuring genetic dissimilarity between diploid individuals with dominant markers can be proposed, and rough estimates might be all that is possible. Banding patterns of diploids with dominant markers represent individuals’ phenotypes rather than genotypes.

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Detection and quantification of soil-borne inoculum of Spongospora subterranea f. sp. subterranea, the cause of powdery scab disease of potato

Jane Tuohy, Xinshun Qu, Damian Egan, James Kavanagh and Fiona Doohan

Molecular Plant Microbe Interactions Group, Department of Environmental Resource Management, Faculty of Agriculture, University College Dublin, Belfield, Dublin 4, Ireland.  (Fax: +35317161102

The purpose of this study was to determine whether a quantitative PCR based method could be used to determine the Spongospora subterranea f. sp. subterranea cystosori content in different types of soils and in naturally infested field soils.  Non-infested soils differing in texture from three different locations in Ireland were spiked with concentrations of cystosori (50 - 5x104 g soil-1).  Spongospora subterranea was detected in soil following extraction of DNA using an UltraClean TM Soil DNA kit and S. subterranea-specific polymerase chain reaction (PCR) analysis that resulted in a 434 bp product.  Quantitative PCR was performed using a heterologous competitor template (541 bp) and standard curves were constructed for each soil type.  No significant differences were found in PCR product ratios of the three spiked soils at any of the cystosori concentrations tested even at the lowest concentration (50 cystosori g soil-1). For each of ten field soils and two garden soil samples cystosori content extrapolated from each standard curve were not significantly different.

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The importance of “diagnostic reasoning” in developing scenarios to train extension pathologists

Terry Stewart

Institute of Natural Resources, Massey University, Palmerston North, New Zealand t.stewart@massey.ac.nz

Plant disease diagnosis is both an art and a science, and it requires considerable experience for a practitioner to become skilled at the task. Students can acquire some appreciation of the process by working through scenarios or diagnostic cases. In educational terms this facilitates “goal-based” or “problem-based” learning, an effective learning paradigm that has gained wide acceptance amongst educationalists. In order for effective teaching scenarios to be developed, an appreciation of diagnosis reasoning is necessary. Little work has been done on this subject in plant pathology, in contrast to medicine where clinical diagnostic skills are very important. Diagnostic models developed including the Select and Test (S-T) Model, and ones describing “forward” and “backward” diagnostic reasoning. The models show, upon the first observation of a disease, a diagnostician would take those observations and form hypotheses which may explain the causes behind the signs and symptoms (abduction). Deduction is then used to narrow down the cause by seeking data which supports the hypotheses. New data and new hypotheses may be created in this process. Heuristics appear to differ between expert and non-expert. Non-experts tend to use non-selective seek and search patterns which can be poor at hypothesis creation. On the other hand, experts recognise patterns, and can generate hypotheses quickly, on the basis of just a few observations. Research suggests that only seven hypotheses are active at any one time during any diagnostic episode constrained by the limited capacity of short-term memory. Plant diagnostic scenarios used as problem-solving exercises should allow students to undertake the diagnostic reasoning explained in these models.  In other words, students, at the level of knowledge being taught, should have the opportunity to form hypotheses on the basis of the initial scenario information and explore them! An electronic template has been produced which assists authors to develop scenarios of this type. Using the template, scenarios are constructed step-by-step, teasing out which hypotheses are likely to be explored and the tasks and observations required to confirm and/or discount them.  Although originally designed to assist authors with developing scenarios for the scenario-based training package Diagnosis for Crop Problems (http://www.diagnosis.co.nz), the template can be used on its own to develop scenarios to be delivered via any teaching medium.

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How complex is the Cercospora apii complex?

Johannes Z. (Ewald) Groenewald1 , Uwe Braun2 and Pedro W. Crous1

1 Centraalbureau voor Schimmelcultures, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands.

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