We investigated the uptake of cerium (Ce) dioxide nanoparticles (NPs) by hydroponically grown wheat, pumpkin and sunflower plants. The presence of plant roots in nutrient solution led to a substantial increase in the dissolution of CeO2-NP compared to plant-free medium. Experiments with Zr/CeOx-NP revealed that Ce was not only taken up in the form of NPs, but simultaneously to a significant degree also as dissolved Ce(iii) ions, which then re-precipitated in the form of CeO2-NPs inside the leaves. The contribution of dissolved Ce uptake was particularly large for particles smaller than 10 nm due to their higher dissolution rate. Our data also indicate that the translocation of Ce resulting from NP-root-exposure is species dependent. When Ce was supplied as dissolved ions, sunflower had the highest capacity of Ce-ion accumulation inside the leaves, while there was no significant difference between pumpkin and wheat. We found no Ce translocation from roots into shoots when only NPs bigger than 20 nm were applied. This study highlights that plant root activity can have a significant impact on the dissolution of CeO2-NPs in soil solution and that uptake of dissolved Ce(iii) followed by re-precipitation needs to be considered as an important pathway in studies of CeO2-NP uptake by plants.
Activity recognition using off-the-shelf smartwatches is an important problem in humanactivity recognition. In this paper, we present an end-to-end deep learning approach, able to provideprobability distributions over activities from raw sensor data. We apply our methods to 10 complexfull-body exercises typical in CrossFit, and achieve a classification accuracy of 99.96%. We additionallyshow that the same neural network used for exercise recognition can also be used in repetitioncounting. To the best of our knowledge, our approach to repetition counting is novel and performswell, counting correctly within an error of 1 repetitions in 91% of the performed sets.
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