Local adaptation drives the diversification of effectors in the fungal wheat pathogen Parastagonospora nodorum in the United States

Filamentous fungi rapidly evolve in response to environmental selection pressures in part due to their genomic plasticity. Parastagonospora nodorum, a fungal pathogen of wheat and causal agent of septoria nodorum blotch, responds to selection pressure exerted by its host, influencing the gain, loss, or functional diversification of virulence determinants, known as effector genes. Whole genome resequencing of 197 P. nodorum isolates collected from spring, durum, and winter wheat production regions of the United States enabled the examination of effector diversity and genomic regions under selection specific to geographically discrete populations. 1,026,859 SNPs/InDels were used to identify novel loci, as well as SnToxA and SnTox3 as factors in disease. Genes displaying presence/absence variation, predicted effector genes, and genes localized on an accessory chromosome had significantly higher pN/pS ratios, indicating a higher rate of sequence evolution. Population structure analyses indicated two P. nodorum populations corresponding to the Upper Midwest (Population 1) and Southern/Eastern United States (Population 2). Prevalence of SnToxA varied greatly between the two populations which correlated with presence of the host sensitivity gene Tsn1 in the most prevalent cultivars in the corresponding regions. Additionally, 12 and 5 candidate effector genes were observed to be under diversifying selection among isolates from Population 1 and 2, respectively, but under purifying selection or neutrally evolving in the opposite population. Selective sweep analysis revealed 10 and 19 regions that had recently undergone positive selection in Population 1 and 2, respectively, involving 92 genes in total. When comparing genes with and without presence/absence variation, those genes exhibiting this variation were significantly closer to transposable elements. Taken together, these results indicate that P. nodorum is rapidly adapting to distinct selection pressures unique to spring and winter wheat production regions by rapid adaptive evolution and various routes of genomic diversification, potentially facilitated through transposable element activity

Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF) in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of the risk of SNB, facilitating sound disease management decisions prior to planting of wheat

Power, Pathological Worldviews, and the Strengths Perspective in Social Work

This article takes up Blundo’s (2001) assertion in this journal that in order to practice from the strengths perspective, social workers need to alter their “frames.” Expanding on this assertion, we specify a particular frame that requires change: a pathological worldview. Examining the strengths perspective with regard to a Foucauldian analysis of power, we argue that to thoroughly implement the strengths perspective, we need to consider the dividing practices that allow us to maintain power and that reflect a pathological worldview. This article provides considerations for social work practice that will be of interest to practicing social workers and social work educators interested in continuing to develop their strengths-based practice

Measurement of infection efficiency of a major wheat pathogen using time-resolved imaging of disease progress

Infection efficiency is a key epidemiological parameter that determines the proportion of pathogen spores able to infect and cause lesions once they have landed on a susceptible plant tissue. In this study, an improved method to measure infection efficiency of Zymoseptoria tritici using a replicated greenhouse experiment is presented. Zymoseptoria tritici is a fungal pathogen that infects wheat leaves and causes septoria tritici blotch (STB), a major disease of wheat worldwide. A novel experimental setup was devised, where living wheat leaves were attached to metal plates, allowing for time‐resolved imaging of disease progress in planta. Because lesions were continuously appearing, expanding and merging during the period of up to 3 weeks, daily measurements were necessary for accurate counting of lesions. Reference membranes were also used to characterize the density and spatial distribution of spores inoculated onto leaf surfaces. In this way, the relationship between the number of lesions and the number of viable spores deposited on the leaves was captured and an infection efficiency of about 4% was estimated from the slope of this relationship. This study provides a proof of principle for accurate and reliable measurement of infection efficiency of Z. tritici. The method opens opportunities for determining the genetic basis of the component of quantitative resistance that suppresses infection efficiency. This knowledge would improve breeding for quantitative resistance against STB, a control measure considered more durable than deployment of major resistance genes

Organizational Considerations in the Application of Budgeting and Cost Effectiveness Systems to Social Welfare Organizations

Social welfare organizations have distinctive organizational characteristics which hinder their adaptability to budget and cost effectiveness systems. This paper identifies those characteristics and discusses their significance