Inference of Convergent Gene Acquisition Among Pseudomonas syringae Strains Isolated From Watermelon, Cantaloupe, and Squash

Pseudomonas syringae sensu strict , (phylogroup 2; referred to as P. syringae) consists of an environmentally ubiquitous bacterial population associated with diseases of numerous plant species. Recent studies using multilocus sequence analysis have indicated the clonal expansion of several P. syringae lineages, located in phylogroups 2a and 2b, in association with outbreaks of bacterial spot disease of watermelon, cantaloupe, and squash in the United States. To investigate the evolutionary processes that led to the emergence of these epidemic lineages, we sequenced the genomes of six P. syringae strains that were isolated from cucurbits grown in the United States, Europe, and China over a period of more than a decade, as well as eight strains that were isolated from watermelon and squash grown in six different Florida counties during the 2013 and 2014 seasons. These data were subjected to comparative analyses along with 42 previously sequenced genomes of P. syringae stains collected from diverse plant species and environments available from GenBank. Maximum likelihood reconstruction of the P. syringae core genome revealed the presence of a hybrid phylogenetic group, comprised of cucurbit strains collected in Florida, Italy, Serbia, and France, which emerged through genome-wide homologous recombination between phylogroups 2a and 2b. Functional analysis of the recombinant core genome showed that pathways involved in the ATP-dependent transport and metabolism of amino acids, bacterial motility, and secretion systems were enriched for recombination. A survey of described virulence factors indicated the convergent acquisition of several accessory type 3 secreted effectors (T3SEs) among phylogenetically distinct lineages through integrative and conjugative element and plasmid loci. Finally, pathogenicity assays on watermelon and squash showed qualitative differences in virulence between strains of the same clonal lineage, which correlated with T3SEs acquired through various mechanisms of horizontal gene transfer (HGT). This study provides novel insights into the interplay of homologous recombination and HGT toward pathogen emergence and highlights the dynamic nature of P. syringae sensu lato genomes

Temporal Incidence of Eriophyid Mites on Rose Rosette Disease-Symptomatic and -Asymptomatic Roses in Central Georgia, USA

Phyllocoptes fructiphilus Keifer (Acari: Eriophyidae) is the vector of rose rosette virus (RRV), which causes rose rosette disease (RRD) in North America. The RRD symptoms, such as witches’ broom, flower, and leaf deformation, disrupt the aesthetic appearance of plants and cause plant mortality. Because there is no cure for RRV, it is critical to manage the vector and reduce the spread of the virus. The information on the phenology of P. fructiphilus on rose plants is essential to develop management strategies and reduce its spread. Thus, the objectives of the study were to determine 1) the phenology of eriophyid mites (including P. fructiphilus) in central Georgia due to its widespread occurrence in the state and 2) the incidence of eriophyid mites on closed and opened flower buds and other plant parts. In central Georgia, eriophyid mites, including P. fructiphilus were active on both symptomatic and asymptomatic plants from April to December. The mite densities were greater during July and August than during the remaining months on asymptomatic plants. The mites were more abundant on the RRD-symptomatic than on the asymptomatic plants. Similar numbers of eriophyid mites were observed on closed and opened flower buds. Eriophyid mite densities were greater on sepals and leaf bases than on other plant parts

Table_2_Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.docx

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model’s accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.</p

Table_4_Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.docx

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model’s accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.</p

Table_5_Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.docx

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model’s accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.</p

Image_2_Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.pdf

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model’s accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.</p

Table_8_Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.docx

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model’s accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.</p