Efficient uptake of Yersinia pseudotuberculosis into cultured mammalian cells is the result of high‐affinity binding of invasin to β1 chain integrins. We demonstrate here that uptake requires Rac1 and Arp 2/3 function. Bacterial uptake was stimulated by GTPγS, but was inhibited in mammalian cells transfected with the interfering Rac1‐N17 derivative. Rac1 was found to be activated in response to integrin engagement by invasin, whereas Rac1 and Arp 2/3 were found to be intensely localized around phagosomes bearing bacteria, indicating a specific role for Rac1 signalling from the nascent phagosome to downstream effectors. To determine whether the Arp 2/3 complex was a component of this proposed pathway, cells overproducing various derivatives of Scar1/WAVE1, an Arp 2/3‐binding protein, were analysed. Sequestration of Arp 2/3 away from the phagocytic cup as a result of Scar1/WAVE1 overproduction dramatically inhibited uptake. To determine whether signalling from Rac1 to Arp 2/3 occurred via N‐WASP, uptake was analysed in a cell line lacking expression of WASP and N‐WASP. Uptake was unaffected by the absence of these proteins, indicating that β1 integrin signalling from Rac1 to Arp 2/3 can occur in the absence of N‐WASP function.

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Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction.

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