Insect herbivores from different feeding guilds induce different signaling pathways in plants. In this study, we examined the effects of salicylic acid (SA)-and jasmonic acid (JA)-mediated defenses on performance of insect herbivores from two different feeding guilds: cellcontent feeders, soybean thrips and phloem feeders, soybean aphids. We used a combination of RT-qPCR analysis and elicitor-induced plant resistance to determine induction of SA and JA signaling pathways and the impact on herbivore performance. In the early interaction between the host plant and the two herbivores, SA and JA signaling seems to occur simultaneously. But overall, soybean thrips induced JA-related marker genes, whereas soybean aphids increased SA and ABA-related marker genes over a 24-h period. Populations of both soybean thrips and soybean aphids were reduced (47 and 25 %, respectively) in methyl jasmonate (MeJA)-pretreated soybean plants. SA treatment has no effect on either herbivore performance. A combination pretreatment of SA and MeJA did not impact soybean thrips population but reduced soybean aphid numbers which was comparable with MeJA treatment. Our data suggest that SA-JA antagonism could be responsible for the effect of hormone pretreatment on thrips performance, but not on aphid performance. By linking plant defense gene expression and elicitor-induced resistance, we were able to pinpoint the role for JA signaling pathway in resistance to two herbivores from different feeding guilds.

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There is increasing interest in the development of gate-based quantum circuits for the training of machine learning models. Yet, little is understood concerning the parameters of circuit design, and the effects of noise and other measurement errors on the performance of quantum machine learning models. In this paper, we explore the practical implications of key circuit design parameters (number of qubits, depth etc.) using several standard machine learning datasets and IBM's Qiskit simulator. In total we evaluate over 6500 unique circuits with n ≈ 120700 individual runs. We find that in general shallow (low depth) wide (more qubits) circuit topologies tend to outperform deeper ones in settings without noise. We also explore the implications and effects of different notions of noise and discuss circuit topologies that are more / less robust to noise for classification machine learning tasks. Based on the findings we define guidelines for circuit topologies that show near-term promise for the realisation of quantum machine learning algorithms using gate-based NISQ quantum computer.