The controversial effects of dehydrated powder of Gracilaria birdiae as a food supplement to juvenile male rats

Journal of Applied Phycology - The present study aimed to characterize in vitro antioxidant properties of red algae (Gracilaria birdiae) powder and to investigate its potential protective and...     

Synergies and trade-offs between insect and pathogen resistance in maize leaves and roots

Determining links between plant defence strategies is important to understand plant evolution and to optimize crop breeding strategies. Although several examples of synergies and trade-offs between defence traits are known for plants that are under attack by multiple organisms, few studies have attempted to measure correlations of defensive strategies using specific single attackers. Such links are hard to detect in natural populations because they are inherently confounded by the evolutionary history of different ecotypes. We therefore used a range of 20 maize inbred lines with considerable differences in resistance traits to determine if correlations exist between leaf and root resistance against pathogens and insects. Aboveground resistance against insects was positively correlated with the plant's capacity to produce volatiles in response to insect attack. Resistance to herbivores and resistance to a pathogen, on the other hand, were negatively correlated. Our results also give first insights into the intraspecific variability of root volatiles release in maize and its positive correlation with leaf volatile production. We show that the breeding history of the different genotypes (dent versus flint) has influenced several defensive parameters. Taken together, our study demonstrates the importance of genetically determined synergies and trade-offs for plant resistance against insects and pathogens.     

SeedQuant: a deep learning-based tool for assessing stimulant and inhibitor activity on root parasitic seeds

Witchweeds (Striga spp.) and broomrapes (Orobanchaceae and Phelipanche spp.) are root parasitic plants that infest many crops in warm and temperate zones, causing enormous yield losses and endangering global food security. Seeds of these obligate parasites require rhizospheric, host-released stimulants to germinate, which opens up possibilities for controlling them by applying specific germination inhibitors or synthetic stimulants that induce lethal germination in the host’s absence. To determine their effect on germination, root exudates or synthetic stimulants/inhibitors are usually applied to parasitic seeds in in vitro bioassays, followed by assessment of germination ratios. Although these protocols are very sensitive, the germination recording process is laborious, representing a challenge for researchers and impeding high-throughput screens. Here, we developed an automatic seed census tool to count and discriminate germinated seeds (GS) from non-GS. We combined deep learning, a powerful data-driven framework that can accelerate the procedure and increase its accuracy, for object detection with computer vision latest development based on the Faster Region-based Convolutional Neural Network algorithm. Our method showed an accuracy of 94% in counting seeds of Striga hermonthica and reduced the required time from approximately 5 min to 5 s per image. Our proposed software, SeedQuant, will be of great help for seed germination bioassays and enable high-throughput screening for germination stimulants/inhibitors. SeedQuant is an open-source software that can be further trained to count different types of seeds for research purposes.