Flupyradifurone resistance in Myzus persicae populations from peach and tobacco in Greece.

BACKGROUND: Myzus persicae has evolved resistance to various insecticides in Greece. Here we examine the effectiveness of the insecticide flupyradifurone against aphid clones collected from tobacco and peach in Greece during 2017-2020. Furthermore, we monitored the frequency of the neonicotinoid resistance mutation R81T in the sampled clones, and the association between the responses to flupyradifurone and acetamiprid. RESULTS: Of 43 clones tested with flupyradifurone, 6.977%, 60.465% and 32.558% showed low (10-14), moderate (19-89) and high (104-1914) resistance factor (RF) values, respectively. Resistance was higher in clones from peach than from tobacco with 42.308% and 17.647% of clones (respectively) failing into the high RF category (median RF values 67.5 and 36.4 for clones from peach and tobacco, respectively). Acetamiprid resistance was detected in clones collected in 2019-2020, in line with our previous study in Greece. The analysis of the whole dataset (54 clones collected during 2017-2020) revealed that all tobacco clones had RF < 7.5, whereas 55.263%, 18.421% and 26.316% of the peach clones exhibited low (<12), moderate (20-48) and high (100-145) RF values, respectively. A significant but moderate association between flupyradifurone and acetamiprid responses was detected (r = 0.513, P < 0.001). The R81T mutation was detected in aphids from peach (5.6% and 32.6% as homozygotes and heterozygotes, respectively) and in one aphid specimen (heterozygote) from tobacco. R81T was partially associated with the resistance to both insecticides, but many highly resistant clones did not possess the mutation, indicating the possible operation of one or more alternative underlying resistance mechanisms. CONCLUSIONS: The use of flupyradifurone and acetamiprid in IPM/IRM should be based on further ongoing susceptibility monitoring. © 2021 Society of Chemical Industry.

Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events.

A molecular initiating event (MIE) is the gateway to an adverse outcome pathway (AOP), a sequence of events ending in an adverse effect. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to chemical risk assessment. For 90 biological targets representing important human MIEs, structural alert-based models have been constructed with an automated procedure that uses Bayesian statistics to iteratively select substructures. These models give impressive average performance statistics (an average of 92% correct predictions across targets), significantly improving on previous models. Random Forest models have been constructed from physicochemical features for the same targets, giving similarly impressive performance statistics (93% correct predictions). A key difference between the models is interpretation of predictions-the structural alert models are transparent and easy to interpret, while Random Forest models can only identify the most important physicochemical features for making predictions. The two complementary models have been combined in a consensus model, improving performance compared to each individual model (94% correct predictions) and increasing confidence in predictions. Variation in model performance has been explained by calculating a modelability index (MODI), using Tanimoto coefficient between Morgan fingerprints to identify nearest neighbor chemicals. This work is an important step toward building confidence in the use of in silico tools for assessment of toxicity.

Trajectory inference and parameter estimation in stochastic models with temporally aggregated data.

Stochastic models are of fundamental importance in many scientific and engineering applications. For example, stochastic models provide valuable insights into the causes and consequences of intra-cellular fluctuations and inter-cellular heterogeneity in molecular biology. The chemical master equation can be used to model intra-cellular stochasticity in living cells, but analytical solutions are rare and numerical simulations are computationally expensive. Inference of system trajectories and estimation of model parameters from observed data are important tasks and are even more challenging. Here, we consider the case where the observed data are aggregated over time. Aggregation of data over time is required in studies of single cell gene expression using a luciferase reporter, where the emitted light can be very faint and is therefore collected for several minutes for each observation. We show how an existing approach to inference based on the linear noise approximation (LNA) can be generalised to the case of temporally aggregated data. We provide a Kalman filter (KF) algorithm which can be combined with the LNA to carry out inference of system variable trajectories and estimation of model parameters. We apply and evaluate our method on both synthetic and real data scenarios and show that it is able to accurately infer the posterior distribution of model parameters in these examples. We demonstrate how applying standard KF inference to aggregated data without accounting for aggregation will tend to underestimate the process noise and can lead to biased parameter estimates.