PACRAT: pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription.

The SARS-CoV-2 pandemic underscored the need for early, rapid, and widespread pathogen detection tests that are readily accessible. Many existing rapid isothermal detection methods use the recombinase polymerase amplification (RPA), which exhibits polymerase chain reaction (PCR)-like sensitivity, specificity, and even higher speed. However, coupling RPA to other enzymatic reactions has proven difficult. For the first time, we demonstrate that with tuning of buffer conditions and optimization of reagent concentrations, RPA can be cascaded into an in vitro transcription reaction, enabling detection using fluorescent aptamers in a one-pot reaction. We show that this reaction, which we term PACRAT (pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription) can be used to detect SARS-CoV-2 RNA with single-copy detection limits, Escherichia coli with single-cell detection limits, and 10-min detection times. Further demonstrating the utility of our one-pot, cascaded amplification system, we show PACRAT can be used for multiplexed detection of the pathogens SARS-CoV-2 and E. coli, along with multiplexed detection of two variants of SARS-CoV-2.

Nucleic acid hybridization-based detection of pathogenic RNA using microscale thermophoresis.

Infectious diseases are one of the world's leading causes of morbidity. Their rapid spread emphasizes the need for accurate and fast diagnostic methods for large-scale screening. Here, we describe a robust method for the detection of pathogens based on microscale thermophoresis (MST). The method involves the hybridization of a fluorescently labeled DNA probe to a target RNA and the assessment of thermophoretic migration of the resulting complex in solution within a 2 to 30-time window. We found that the thermophoretic migration of the nucleic acid-based probes is primarily determined by the fluorescent molecule used, rather than the nucleic acid sequence of the probe. Furthermore, a panel of uniformly labeled probes that bind to the same target RNA yields a more responsive detection pattern than a single probe, and moreover, can be used for the detection of specific pathogen variants. In addition, intercalating agents (ICA) can be used to alter migration directionality to improve detection sensitivity and resolving power by several orders of magnitude. We show that this approach can rapidly diagnose viral SARS-CoV2, influenza H1N1, artificial pathogen targets, and bacterial infections. Furthermore, it can be used for anti-microbial resistance testing within 2 h, demonstrating its diagnostic potential for early pathogen detection.

GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences.

Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.

In-vitro Clinical Diagnostics using RNA-Cleaving DNAzymes.

Over the last three decades, significant advancements have been made in the development of biosensors and bioassays that use RNA-cleaving DNAzymes (RCDs) as molecular recognition elements. While early examples of RCDs were primarily responsive to metal ions, the past decade has seen numerous RCDs reported for more clinically relevant targets such as bacteria, cancer cells, small metabolites, and protein biomarkers. Over the past 5 years several RCD-based biosensors have also been evaluated using either spiked biological matrixes or patient samples, including blood, serum, saliva, nasal mucus, sputum, urine, and faeces, which is a critical step toward regulatory approval and commercialization of such sensors. In this review, an overview of the methods used to generate RCDs and the properties of key RCDs that have been utilized for in vitro testing is first provided. Examples of RCD-based assays and sensors that have been used to test either spiked biological samples or patient samples are then presented, highlighting assay performance in different biological matrixes. A summary of current prospects and challenges for development of in vitro diagnostic tests incorporating RCDs and an overview of future directions of the field is also provided.

Simultaneous identification of DNA and RNA pathogens using metagenomic sequencing in cases of severe acute respiratory infection.

Metagenomic next-generation sequencing (mNGS) is a valuable technique for identifying pathogens. However, conventional mNGS requires the separate processing of DNA and RNA genomes, which can be resource- and time-intensive. To mitigate these impediments, we propose a novel method called DNA/RNA cosequencing that aims to enhance the efficiency of pathogen detection. DNA/RNA cosequencing uses reverse transcription of total nucleic acids extracted from samples by using random primers, without removing DNA, and then employs mNGS. We applied this method to 85 cases of severe acute respiratory infections (SARI). Influenza virus was identified in 13 cases (H1N1: seven cases, H3N2: three cases, unclassified influenza type: three cases) and was not detected in the remaining 72 samples. Bacteria were present in all samples. Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii were detected in four influenza-positive samples, suggesting coinfections. The sensitivity and specificity for detecting influenza A virus were 73.33% and 95.92%, respectively. A κ value of 0.726 indicated a high level of concordance between the results of DNA/RNA cosequencing and SARI influenza virus monitoring. DNA/RNA cosequencing enhanced the efficiency of pathogen detection, providing a novel capability to strengthen surveillance and thereby prevent and control infectious disease outbreaks.

Recent advances in the biosensors application for reviving infectious disease management in silkworm model: a new way to combat microbial pathogens.

Silkworms are an essential economic insect but are susceptible to diseases during rearing, leading to yearly losses in cocoon production. While chemical control is currently the primary method to reduce disease incidences, its frequent use can result in loss of susceptibility to pathogens and, ultimately, antibiotic resistance. To effectively prevent or control disease, growers must accurately, sensitively, and quickly detect causal pathogens to determine the best management strategies. Accurate recognition of diseased silkworms can prevent pathogen transmission and reduce cocoon loss. Different pathogen detection methods have been developed to achieve this objective, but they need more precision, specificity, consistency, and promptness and are generally unsuitable for in-situ analysis. Therefore, detecting silkworm diseases under rearing conditions is still an unsolved problem. As a consequence of this, there is an enormous interest in the development of biosensing systems for the early and precise identification of pathogens. There is also significant room for improvement in translating novel biosensor techniques to identify silkworm pathogens. This study explores the types of silkworm diseases, their symptoms, and their causal microorganisms. Moreover, we compare the traditional approaches used in silkworm disease diagnostics along with the latest sensing technologies, with a precise emphasis on lateral flow assay-based biosensors that can detect and manage silkworm pathogens.

Discovery of Vibrio cholerae in Urban Sewage in Copenhagen, Denmark.

We report the discovery of a persistent presence of Vibrio cholerae at very low abundance in the inlet of a single wastewater treatment plant in Copenhagen, Denmark at least since 2015. Remarkably, no environmental or locally transmitted clinical case of V. cholerae has been reported in Denmark for more than 100 years. We, however, have recovered a near-complete genome out of 115 metagenomic sewage samples taken over the past 8 years, despite the extremely low relative abundance of one V. cholerae read out of 500,000 sequenced reads. Due to the very low relative abundance, routine screening of the individual samples did not reveal V. cholerae. The recovered genome lacks the gene responsible for cholerae toxin production, but although this strain may not pose an immediate public health risk, our finding illustrates the importance, challenges, and effectiveness of wastewater-based pathogen surveillance.

Clinical evaluation of a multiplex droplet digital PCR for pathogen detection in critically ill COVID-19 patients with bloodstream infections.

BACKGROUND: Nosocomial bloodstream infections (nBSI) have emerged as a clinical concern for physicians treating COVID-19 patients. In this study, we aimed to evaluate the effectiveness of a multiplex ddPCR in detecting bacterial pathogens in the blood of COVID-19 critically ill patients. METHODS: This prospective diagnostic study included RT-PCR-confirmed COVID-19 patients admitted to our hospital from December 2022 to February 2023. A multiplex ddPCR assay was used to detect common bacterial pathogens and AMR genes in blood samples of the patients, along with antimicrobial susceptibility testing (AST). The diagnostic performance of the ddPCR assay was evaluated by comparing the results with those obtained through blood culture and clinical diagnosis. Additionally, the ability of ddPCR in detecting bacterial resistance was compared with the AST results. RESULTS: Of the 200 blood samples collected from 184 patients, 45 (22.5%) were positive using blood culture, while 113 (56.5%) were positive for bacterial targets using the ddPCR assay. The ddPCR assay outperformed blood culture in pathogen detection rate, mixed infection detection rate, and fungal detection rate. Acinetobacter baumannii and Klebsiella pneumoniae were the most commonly detected pathogens in COVID-19 critically ill patients, followed by Enterococcus and Streptococcus. Compared to blood culture, ddPCR achieved a sensitivity of 75.5%, specificity of 51.0%, PPV of 30.9%, and NPV of 87.8%, respectively. However, there were significant differences in sensitivity among different bacterial species, where Gram-negative bacteria have the highest sensitivity of 90.3%. When evaluated on the ground of clinical diagnosis, the sensitivity, specificity, PPV and NPV of ddPCR were 78.1%, 90.5%, 94.7%, and 65.5%, respectively. In addition, the ddPCR assay detected 23 cases of blaKPC, which shown a better consistent with clinical test results than other detected AMR genes. Compared to blaKPC, there were few other AMR genes detected, indicating that the application of other AMR gene detection in the COVID-19 critically ill patients was limited. CONCLUSION: The multiplex ddPCR assay had a significantly higher pathogen detection positivity than the blood culture, which could be an effective diagnostic tool for BSIs in COVID-19 patients and to improve patient outcomes and reduce the burden of sepsis on the healthcare system, though there is room for optimization of the panels used.- Adjusting the targets to include E. faecalis and E. faecium as well as Candida albicans and Candida glabrata could improve the ddPCR' s effectiveness. However, further research is needed to explore the potential of ddPCR in predicting bacterial resistance through AMR gene detection.

Recombinant mannan-binding lectin magnetic beads increase pathogen detection in immunocompromised patients.

The microbiological diagnosis of infection for hematological malignancy patients receiving chemotherapy or allogeneic hematopoietic stem cell transplantation (allo-HSCT) patients relies primarily on standard microbial culture, especially blood culture, which has many shortcomings, such as having low positive rates, being time-consuming and having a limited pathogenic spectrum. In this prospective observational self-controlled test accuracy study, blood, cerebrospinal fluid (CSF), and bronchoalveolar lavage fluid (BALF) samples were collected from chemotherapy or allo-HSCT patients with clinical symptoms of infections who were hospitalized at Peking University First Hospital. Possible pathogens were detected by the method based on recombinant mannan-binding lectin (MBL) magnetic bead enrichment (M1 method) and simultaneously by a standard method. The analytical sensitivity of M1 method was close to that of standard culture method. Besides, the turn-around time of M1-method was significantly shorter than that of standard culture method. Moreover, the M1 method also added diagnostic value through the detection of some clinically relevant microbes missed by the standard method. M1 method could significantly increase the detection efficiency of pathogens (including bacteria and fungi) in immunocompromised patients. KEY POINTS: • The detection results of M1-method had a high coincidence rate with that of standard method • M1 method detected many pathogens which had not been found by standard clinic method.

A Survey of Xylella fastidiosa in the U.S. State of Virginia Reveals Wide Distribution of Both Subspecies fastidiosa and multiplex in Grapevine.

Global travel and trade in combination with climate change are expanding the geographic distribution of plant pathogens. The bacterium Xylella fastidiosa is a prime example. Native to the Americas, it has spread to Europe, Asia, and the Middle East. To assess the risk that pathogen introductions pose to crops in newly invaded areas, it is key to survey their diversity, host range, and disease incidence in relation to climatic conditions where they are already present. We performed a survey of X. fastidiosa in grapevine in Virginia using a combination of quantitative PCR, multilocus sequencing, and metagenomics. We also analyzed samples from deciduous trees with leaf scorch symptoms. X. fastidiosa subspecies fastidiosa was identified in grapevines in all regions of the state, even in Northern Virginia, where the temperature was below -9°C for 10 days per year on average in the years preceding sampling. Unexpectedly, we also found for the first time grapevine samples infected with X. fastidiosa subspecies multiplex (Xfm). The Xfm lineage found in grapevines had been previously isolated from blueberries in the Southeastern United States and was distinct from that found in deciduous trees in Virginia. The obtained results will be important for risk assessment of X. fastidiosa introductions in other parts of the world.

A Polyvalent Tool for Detecting Coguviruses in Multiple Hosts Allowed the Identification of a Novel Seed-Transmitted Coguvirus Infecting Brassicaceae.

The genus Coguvirus, a recently established genus in the family Phenuiviridae, includes several species whose members infect both woody and herbaceous hosts, suggesting a broader host range and wider distribution than previously. To gain insights into the epidemiology and biology of coguviruses, a polyvalent reverse transcription-PCR assay using degenerate primers was developed. The specificity of the assay for coguviruses was confirmed by testing citrus and apple plants infected by previously reported coguviruses and/or several unrelated viruses. The expected 236-bp amplicon was obtained from citrus, apple, pear, watermelon, and several species of the family Brassicaceae. Sequencing of the PCR amplicons allowed the identification, for the first time in Italy and/or Europe, of several coguviruses in multiple hosts, confirming the effectiveness of the assay. Moreover, a new virus, tentatively named Brassica oleracea Torzella virus 1 (BoTV1), was detected in several plants of Torzella cabbage. The complete +genome of BoTV1, determined by high-throughput sequencing and 5' rapid amplification of cDNA ends, revealed that it has the typical molecular features of coguviruses and fulfils the current criteria to be classified as a member of a new species, for which the tentative name Coguvirus torzellae is proposed. The same polyvalent assay was also used to investigate and confirm that BoTV1 is transmitted through seeds in black cabbage, thus providing the first evidence on the relevance of this natural transmission mode in the epidemiology of coguviruses. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Rapid Detection of Viral, Bacterial, Fungal, and Oomycete Pathogens on Tomatoes with Microneedles, LAMP on a Microfluidic Chip, and Smartphone Device.

Rapid detection of plant diseases before they escalate can improve disease control. Our team has developed rapid nucleic acid extraction methods with microneedles and combined these with loop-mediated amplification (LAMP) assays for pathogen detection in the field. In this work, we developed LAMP assays for early blight (Alternaria linariae, A. alternata, and A. solani) and bacterial spot of tomato (Xanthomonas perforans) and validated these LAMP assays and two previously developed LAMP assays for tomato spotted wilt virus and late blight. Tomato plants were inoculated, and disease severity was measured. Extractions were performed using microneedles, and LAMP assays were run in tubes (with hydroxynaphthol blue) on a heat block or on a newly designed microfluidic slide chip on a heat block or a slide heater. Fluorescence on the microfluidic chip slides was visualized using EvaGreen and photographed on a smartphone. Plants inoculated with X. perforans or tomato spotted wilt virus tested positive prior to visible disease symptoms, whereas Phytophthora infestans and A. linariae were detected at the time of visual disease symptoms. LAMP assays were more sensitive than PCR, and the limit of detection was 1 pg of DNA for both A. linariae and X. perforans. The LAMP assay designed for early blight detected all three species of Alternaria that infect tomato and is thus an Alternaria spp. assay. This study demonstrates the utility of rapid microneedle extraction followed by LAMP on a microfluidic chip for rapid diagnosis of four important tomato pathogens.

Validation of PCR diagnostic assays for detection and identification of all Ralstonia solanacearum sequevars causing Moko disease in banana.

Moko disease in banana is a bacterial wilt caused by strains within Ralstonia solanacearum sensu stricto. The disease is endemic to Central and South America but has spread to the Philippines and peninsular Malaysia. Detecting new incursions early in Moko-free banana production regions is of utmost importance for containment and eradication, as Moko management significantly increases costs of banana production. Molecular studies have supported the classification of R. solanacearum sensu stricto into phylotypes IIA, IIB and IIC, each comprising of various sequevars based on nucleotide divergence of a partial sequence within the endoglucanase gene. Moko disease in banana is caused by strains classified as sequevars 6, 24, 41, and 53 within phylotype IIA, and sequevars 3, 4, and 25 within phylotype IIB. To ensure accurate diagnostic assays are available to detect all Moko sequevars, we systematically validated previously published assays for Moko diagnostics. To be able to identify all sequevars, including the latest described sequevars, namely IIB-25, IIA-41, and IIA-53, we developed and validated two novel assays using genome-wide association studies on over 100 genomes of R. solanacearum sensu stricto. Validations using 196 bacterial isolates confirmed that a previous multiplex PCR based assay targeting sequevars IIB-3, IIB-4, IIA-6 and IIA-24 and our two novel assays targeting sequevars IIB-25, IIA-41 and IIA-53 were specific, reproducible, and accurate for Moko diagnostics.

The Role of Salicylic Acid in the Expression of RECEPTOR-LIKE PROTEIN 23 and Other Immunity-Related Genes.

The hormone salicylic acid (SA) plays a crucial role in plant immunity by activating responses that arrest pathogen ingress. SA accumulation also penalizes growth, a phenomenon visible in mutants that hyperaccumulate SA, resulting in strong growth inhibition. An important question, therefore, is why healthy plants produce basal levels of this hormone when defense responses are not activated. Here, we show that basal SA levels in unchallenged plants are needed for the expression of a number of immunity-related genes and receptors, such as RECEPTOR-LIKE PROTEIN 23 (RLP23). This was shown by depleting basal SA levels in transgenic Arabidopsis lines through the overexpression of the SA-inactivating hydroxylases DOWNY MILDEW-RESISTANT 6 (DMR6) or DMR6-LIKE OXYGENASE 1. RNAseq analysis revealed that the expression of a subset of immune receptor and signaling genes is strongly reduced in the absence of SA. The biological relevance of this was shown for RLP23: In SA-depleted and SA-insensitive plants, responses to the RLP23 ligand, the microbial pattern nlp24, were strongly reduced, whereas responses to flg22 remained unchanged. We hypothesize that low basal SA levels are needed for the expression of a subset of immune system components that enable early pathogen detection and activation of immunity.

YDV Resistance in Barley: Phenotyping, Remote Imagery, and Virus-Vector Characterization.

Yellow Dwarf Viruses (YDV) spread by aphids are some of the most economically important barley (Hordeum vulgare L.) virus-vector complexes worldwide. Detection and control of these viruses are critical components in the production of barley, wheat, and numerous other grasses of agricultural importance. Genetic control of plant diseases is often preferable to chemical control to reduce the epidemiological, environmental, and economic cost of foliar insecticides. Accordingly, the objectives of this work were to I) screen a barley population for resistance to YDV under natural infection using phenotypic assessment of disease symptoms, II) implement drone imagery to further assess resistance and test its utility as a disease screening tool, III) identify the prevailing virus and vector types in the experimental environment, and IV) perform a genome-wide association study to identify genomic regions associated with measured traits. Significant genetic differences were found in a population of 192 barley inbred lines regarding their YDV symptom severity and symptoms were moderately to highly correlated with grain yield. The severity of YDV measured with aerial imaging was highly correlated with on-the-ground estimates (r=0.65). Three aphid species vectoring three YDV species were identified with no apparent genotypic influence on their distribution. A QTL impacting YDV resistance was detected on chromosome 2H, albeit undetected using aerial imaging. However, QTL for canopy cover and mean NDVI were successfully mapped using the drone. This work provides a framework for utilizing drone imagery in future resistance breeding efforts for YDV in cereals and grasses, and in other virus-vector disease complexes.

TOMMicroNet: Convolutional Neural Networks for Smartphone-Based Microscopic Detection of Tomato Biotic and Abiotic Plant Health Issues.

The image-based detection and classification of plant diseases has become increasingly important to the development of precision agriculture. We consider the case of tomato, a high-value crop supporting the livelihoods of many farmers around the world. Many biotic and abiotic plant health issues impede the efficient production of this crop, and laboratory-based diagnostics are inaccessible in many remote regions. Early detection of these plant health issues is essential for efficient and accurate response, prompting exploration of alternatives for field detection. Considering the availability of low-cost smartphones, artificial intelligence-based classification facilitated by mobile phone imagery can be a practical option. This study introduces a smartphone-attachable 30x microscopic lens, used to produce the novel tomato microimaging dataset of 8500 images representing 34 tomato plant conditions on the upper and lower sides of leaves as well as on the surface of tomato fruits. We introduce TOMMicroNet, a 14-layer convolutional neural network (CNN) trained to classify amongst biotic and abiotic plant health issues, and we compare it against six existing pre-trained CNN models. We compared two separate pipelines of grouping data for training TOMMicroNet, either presenting all data at once or separating into subsets based on the three parts of the plant. Comparing configurations based on cross-validation and F1 scores, we determined that TOMMicroNet attained the highest performance when trained on the complete dataset, with 95% classification accuracy on both training and external datasets. Given TOMMicroNet's capabilities when presented with unfamiliar data, this approach has the potential for the identification of plant health issues.

Hiding in Plain Sight: A Widespread Native Perennial Harbors Diverse Haplotypes of 'Candidatus Liberibacter solanacearum' and Its Potato Psyllid Vector.

The unculturable bacterium 'Candidatus Liberibacter solanacearum' (CLso) is responsible for a growing number of emerging crop diseases. However, we know little about the diversity and ecology of CLso and its psyllid vectors outside of agricultural systems, which limits our ability to manage crop disease and understand the impacts this pathogen may have on wild plants in natural ecosystems. In North America, CLso is transmitted to crops by the native potato psyllid (Bactericera cockerelli). However, the geographic and host plant range of the potato psyllid and CLso beyond the borders of agriculture are not well understood. A recent study of historic herbarium specimens revealed that a unique haplotype of CLso was present infecting populations of the native perennial Solanum umbelliferum in California decades before CLso was first detected in crops. We hypothesized that this haplotype and other potentially novel CLso variants are still present in S. umbelliferum populations. To test this, we surveyed populations of S. umbelliferum in Southern California for CLso and potato psyllid vectors. We found multiple haplotypes of CLso and the potato psyllid associated with these populations, with none of these genetic variants having been previously reported in California crops. These results suggest that CLso and its psyllid vectors are much more widespread and diverse in North American natural plant communities than suggested by data collected solely from crops and weeds in agricultural fields. Further characterization of these apparently asymptomatic haplotypes will facilitate comparison with disease-causing variants and provide insights into the continued emergence and spread of CLso.

Open Access and Reproducibility in Plant Pathology Research: Guidelines and Best Practices.

The landscape of scientific publishing is experiencing a transformative shift toward open access, a paradigm that mandates the availability of research outputs such as data, code, materials, and publications. Open access provides increased reproducibility and allows for reuse of these resources. This article provides guidance for best publishing practices of scientific research, data, and associated resources, including code, in The American Phytopathological Society journals. Key areas such as diagnostic assays, experimental design, data sharing, and code deposition are explored in detail. This guidance aligns with that observed by other leading journals. We hope the information assembled in this paper will raise awareness of best practices and enable greater appraisal of the true effects of biological phenomena in plant pathology.

New Genotypes of Phytophthora nicotianae Found on Strawberry in Florida.

Phytophthora cactorum is the most common causal agent of Phytophthora crown rot and leather rot of strawberry, but P. nicotianae is also responsible for the disease in Florida. Studies of P. nicotianae populations have suggested that different groups of genotypes are associated with different hosts; however, it is not yet clear how many lineages exist globally and how they are related to different production systems. The aim of this study was to determine the genetic relationships of P. nicotianae isolates from Florida strawberry with genotypes reported from other hosts, quantify the genetic variation on strawberry, and test for an association with nursery source. A total of 49 isolates of P. nicotianae were collected from strawberry plants originating from multiple nursery sources during six seasons of commercial fruit production in Florida. Microsatellite genotyping identified 28 multilocus genotypes on strawberry that were distinct among 208 isolates originating from various hosts and locations. Based on STRUCTURE analysis, two genetic groups were identified: one consisting of isolates from strawberry, and the other comprising samples from different hosts. Multilocus genotypes were shared among nursery sources, and populations defined by nursery were not differentiated. Both mating types were found among the isolates from North Carolina- and California-origin plants and in most strawberry seasons; however, a predominance of A1 was observed, and regular sexual reproduction was not supported by the data. This study reveals a unique genetic population of P. nicotianae associated with strawberry and emphasizes the vital role of nursery monitoring in mitigating disease spread.

Deep Learning Based Barley Disease Quantification for Sustainable Crop Production.

Net blotch disease caused by Drechslera teres is a major fungal disease that affects barley (Hordeum vulgare) plants and can result in significant crop losses. In this study, we developed a deep-learning model to quantify net blotch disease symptoms on different days post-infection on seedling leaves using Cascade R-CNN (Region-Based Convolutional Neural Networks) and U-Net (a convolutional neural network) architectures. We used a dataset of barley leaf images with annotations of net blotch disease to train and evaluate the model. The model achieved an accuracy of 95% for cascade R-CNN in net blotch disease detection and a Jaccard index score of 0.99, indicating high accuracy in disease quantification and location. The combination of Cascade R-CNN and U-Net architectures improved the detection of small and irregularly shaped lesions in the images at 4-days post infection, leading to better disease quantification. To validate the model developed, we compared the results obtained by automated measurement with a classical method (necrosis diameter measurement) and a pathogen detection by real-time PCR. The proposed deep learning model could be used in automated systems for disease quantification and to screen the efficacy of potential biocontrol agents to protect against disease.