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- Gustavsson, Larisa, et al.
(author)
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Resistance to Neonectria ditissima in apple : insights from metabolomics and lipidomics analyses
- 2023
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In: Xxxi international horticultural congress (ihc2022). - : International Society for Horticultural Science. - 9789462613614 ; , s. 329-335, s. 329-336
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Conference paper (peer-reviewed)abstract
- European canker, caused by the necrotrophic fungus Neonectria ditissima, is the most serious disease in apple production in Sweden. The disease is favored by a relatively cool and rainy climate. The canker damages have a significant economic impact due to reduced bearing surface and increased orchard management costs. The possibilities for chemical and biological control are very limited. Therefore, directed breeding for new resistant cultivars is urgently needed. Knowledge of inheritance of canker resistance and understanding of molecular mechanisms involved in resistant and susceptible responses to fungal attacks would facilitate breeding. In this study, we evaluated the tempo-spatial differences in plant-pathogen interactions in a set of partially resistant and susceptible cultivars by conducting metabolomic and lipidomic analyses. The major trends in metabolomics and lipidomic profiles were common among cultivars, irrespective of the degree of susceptibility. Several metabolites and lipids varied with time point and cultivar under N. ditissima infection. Putative key metabolites such as suberic acid and jasmonic acid were upregulated in all cultivars upon infection. Additionally, several lipids exhibited changes 30 to 45 days post-inoculation. Thus, the approach used seems to have resulted in a rich data set to be further analyzed in light of ongoing QTL-mapping efforts.
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- Rashid, Maheen, et al.
(author)
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Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation
- 2022
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In: 2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 152-162
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Conference paper (peer-reviewed)abstract
- Timely detection of horse pain is important for equine welfare. Horses express pain through their facial and body behavior, but may hide signs of pain from unfamiliar human observers. In addition, collecting visual data with detailed annotation of horse behavior and pain state is both cumbersome and not scalable. Consequently, a pragmatic equine pain classification system would use video of the un-observed horse and weak labels. This paper proposes such a method for equine pain classification by using multi-view surveillance video footage of unobserved horses with induced orthopaedic pain, with temporally sparse video level pain labels. To ensure that pain is learned from horse body language alone, we first train a self-supervised generative model to disentangle horse pose from its appearance and background before using the disentangled horse pose latent representation for pain classification. To make best use of the pain labels, we develop a novel loss that formulates pain classification as a multi-instance learning problem. Our method achieves pain classification accuracy better than human expert performance with 60% accuracy. The learned latent horse pose representation is shown to be viewpoint covariant, and disentangled from horse appearance. Qualitative analysis of pain classified segments shows correspondence between the pain symptoms identified by our model, and equine pain scales used in veterinary practice.
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