Endocytic Trafficking Promotes Vacuolar Enlargements for Fast Cell Expansion Rates in Plants

The vacuole has a space-filling function, allowing a particularly rapid plant cell expansion with very little increase in cytosolic content (Loefke et al., 2015; Scheuring et al., 2016; Duenser et al., 2019). Despite its importance for cell size determination in plants, very little is known about the mechanisms that define vacuolar size. Here we show that the cellular and vacuolar size expansions are coordinated. By developing a pharmacological tool, we enabled the investigation of membrane delivery to the vacuole during cellular expansion. Counterintuitively, our data reveal that endocytic trafficking from the plasma membrane to the vacuole is enhanced in the course of rapid root cell expansion. While this "compromise" mechanism may theoretically at first decelerate cell surface enlargements, it fuels vacuolar expansion and, thereby, ensures the coordinated augmentation of vacuolar occupancy in dynamically expanding plant cells.

Fast Image Classification for Grain Size Determination

With the increasing application of steel materials, the metallographic analysis of steel has gained importance. At present, grain size analysis remains the task of experts who must manually evaluate photos of the structure. Given the software currently available for this task, it is impossible to effectively determine the grain size because of the limitations of traditional algorithms. Artificial intelligence is now being applied in many fields. This paper uses the concept of deep learning to propose a fast image classifier (FIC) to classify grain size. We establish a classification model based on the grain size of steel in metallography. This model boasts high performance, fast operation, and low computational costs. In addition, we use a real metallographic dataset to compare FIC with other deep learning network architectures. The experimental results show that the proposed method yields a classification accuracy of 99.7%, which is higher than existing methods, and boasts computational demands, which are far lower than with other network architectures. We propose a novel system for automatic grain size determination as an application for metallographic analysis.