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Object detection in UAV aerial images faces domain-adaptive challenges, such as changes in shooting height, viewing angle, and weather. These changes constitute a large number of fine-grained domains that place greater demands on the network's generalizability. To tackle these challenges, we initially decompose image features into domain-invariant and domain-specific features using practical imaging condition parameters. The composite feature can improve domain generalization and single-domain accuracy compared to the conventional fine-grained domain-detection method. Then, to solve the problem of the overfitting of high-frequency imaging condition parameters, we mixed images from different imaging conditions in a balanced sampling manner as input for the training of the detection network. The data-augmentation method improves the robustness of training and reduces the overfitting of high-frequency imaging parameters. The proposed algorithm is compared with state-of-the-art fine-grained domain detectors on the UAVDT and VisDrone datasets. The results show that it achieves an average detection precision improvement of 5.7 and 2.4, respectively. The airborne experiments validate that the algorithm achieves a 20 Hz processing performance for 720P images on an onboard computer with Nvidia Jetson Xavier NX. [ABSTRACT FROM AUTHOR]

Acoustic droplet ejection (ADE) is a powerful technology that supports crystallographic applications such as growing, improving and manipulating protein crystals. A fragment-screening strategy is described that uses ADE to co-crystallize proteins with fragment libraries directly on MiTeGen MicroMeshes. Co-crystallization trials can be prepared rapidly and economically. The high speed of specimen preparation and the low consumption of fragment and protein allow the use of individual rather than pooled fragments. The Echo 550 liquid-handling instrument (Labcyte Inc., Sunnyvale, California, USA) generates droplets with accurate trajectories, which allows multiple co-crystallization experiments to be discretely positioned on a single data-collection micromesh. This accuracy also allows all components to be transferred through small apertures. Consequently, the crystallization tray is in equilibrium with the reservoir before, during and after the transfer of protein, precipitant and fragment to the micromesh on which crystallization will occur. This strict control of the specimen environment means that the crystallography experiments remain identical as the working volumes are decreased from the few microlitres level to the few nanolitres level. Using this system, lysozyme, thermolysin, trypsin and stachydrine demethylase crystals were co-crystallized with a small 33-compound mini-library to search for fragment hits. This technology pushes towards a much faster, more automated and more flexible strategy for structure-based drug discovery using as little as 2.5 nl of each major component.