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Counting nematodes is a labor-intensive and time-consuming task, yet it is a pivotal step in various quantitative nematological studies; preparation of initial population densities and final population densities in pot, micro-plot and field trials for different objectives related to management including sampling and location of nematode infestation foci. Nematologists have long battled with the complexities of nematode counting, leading to several research initiatives aimed at automating this process. However, these research endeavors have primarily focused on identifying single-class objects within individual images. To enhance the practicality of this technology, there’s a pressing need for an algorithm that cannot only detect but also classify multiple classes of objects concurrently. This study endeavors to tackle this challenge by developing a user-friendly Graphical User Interface (GUI) that comprises multiple deep learning algorithms, allowing simultaneous recognition and categorization of nematode eggs and second stage juveniles of Meloidogyne spp. In total of 650 images for eggs and 1339 images for juveniles were generated using two distinct imaging systems, resulting in 8655 eggs and 4742 Meloidogyne juveniles annotated using bounding box and segmentation, respectively. The deep-learning models were developed by leveraging the Convolutional Neural Networks (CNNs) machine learning architecture known as YOLOv8x. Our results showed that the models correctly identified eggs as eggs and Meloidogyne juveniles as Meloidogyne juveniles in 94% and 93% of instances, respectively. The model demonstrated higher than 0.70 coefficient correlation between model predictions and observations on unseen images. Our study has showcased the potential utility of these models in practical applications for the future. The GUI is made freely available to the public through the author’s GitHub repository (https://github.com/bresilla/ nematode_counting). While this study currently focuses on one genus, there are plans to expand the GUI’s capabilities to include other economically significant genera of plant parasitic nematodes. Achieving these objectives, including enhancing the models’ accuracy on different imaging systems, may necessitate collaboration among multiple nematology teams and laboratories, rather than being the work of a single entity. With the increasing interest among nematologists in harnessing machine learning, the authors are confident in the potential development of a universal automated nematode counting system accessible to all. This paper aims to serve as a framework and catalyst for initiating global collaboration toward this important goal. [ABSTRACT FROM AUTHOR]

Summary: To establish persistent infections in host plants, herbivorous invaders, such as root‐knot nematodes, must rely on effectors for suppressing damage‐induced jasmonate‐dependent host defenses. However, at present, the effector mechanisms targeting the biosynthesis of biologically active jasmonates to avoid adverse host responses are unknown.Using yeast two‐hybrid, in planta co‐immunoprecipitation, and mutant analyses, we identified 12‐oxophytodienoate reductase 2 (OPR2) as an important host target of the stylet‐secreted effector MiMSP32 of the root‐knot nematode Meloidogyne incognita. MiMSP32 has no informative sequence similarities with other functionally annotated genes but was selected for the discovery of novel effector mechanisms based on evidence of positive, diversifying selection.OPR2 catalyzes the conversion of a derivative of 12‐oxophytodienoate to jasmonic acid (JA) and operates parallel to 12‐oxophytodienoate reductase 3 (OPR3), which controls the main pathway in the biosynthesis of jasmonates. We show that MiMSP32 targets OPR2 to promote parasitism of M. incognita in host plants independent of OPR3‐mediated JA biosynthesis.Artificially manipulating the conversion of the 12‐oxophytodienoate by OPRs increases susceptibility to multiple unrelated plant invaders. Our study is the first to shed light on a novel effector mechanism targeting this process to regulate the susceptibility of host plants. [ABSTRACT FROM AUTHOR]

Summary: Susceptibility to the root‐knot nematode Meloidogyne incognita in plants is thought to be a complex trait based on multiple genes involved in cell differentiation, growth and defence. Previous genetic analyses of susceptibility to M. incognita have mainly focused on segregating dominant resistance genes in crops. It is not known if plants harbour significant genetic variation in susceptibility to M. incognita independent of dominant resistance. To study the genetic architecture of susceptibility to M. incognita, we analysed nematode reproduction on a highly diverse set of 340 natural inbred lines of Arabidopsis thaliana with genome‐wide association mapping. We observed a surprisingly large variation in nematode reproduction among these lines. Genome‐wide association mapping revealed four quantitative trait loci (QTLs) located on chromosomes 1 and 5 of A. thaliana significantly associated with reproductive success of M. incognita, none of which harbours typical resistance gene homologues. Mutant analysis of three genes located in two QTLs showed that the transcription factor BRASSINAZOLE RESISTANT1 and an F‐box family protein may function as (co‐)regulators of susceptibility to M. incognita in Arabidopsis. Our data suggest that breeding for loss‐of‐susceptibility, based on allelic variants critically involved in nematode feeding, could be used to make crops more resilient to root‐knot nematodes. [ABSTRACT FROM AUTHOR]