Seed germination and photoautotrophic establishment are controlled by the antagonistic activity of the phytohormones gibberellins (GAs) and abscisic acid (ABA). Here we show that
Arabidopsis thaliana
GAS2 (Gain of Function in ABA-modulated Seed Germination 2), a protein belonging to the Fe-dependent 2-oxoglutarate dioxygenase superfamily, catalyzes the stereospecific hydration of GA
12
to produce GA
12
16, 17-dihydro-16α-ol (DHGA
12
). We show that DHGA
12
, a C
20
-GA has an atypical structure compared to known active GAs but can bind to the GA receptor (GID1c). DHGA
12
can promote seed germination, hypocotyl elongation and cotyledon greening. Silencing and over-expression of
GAS2
alters the ABA/GA ratio and sensitivity to ABA during seed germination and photoautotrophic establishment. Hence, we propose that GAS2 acts to modulate hormonal balance during early seedling development.
Deep neural networks, such as Faster R-CNN, have been widely used in object detection. However, deep neural networks usually require a large-scale dataset to achieve desirable performance. For the specific application, UAV detection, training data is extremely limited in practice. Since annotating plenty of UAV images manually can be very resource intensive and time consuming, instead, we use PBRT to render a large number of photorealistic UAV images of high variation within a reasonable time. Using PBRT ensures the realism of rendered images, which means they are indistinguishable from real photographs to some extent. Trained with our rendered images, the Faster R-CNN has an AP of 80.69% on manually annotated UAV images test set, much higher than the one only trained with COCO 2014 dataset and PASCAL VOC 2012 dataset (43.36%). Moreover, our rendered image dataset contains not only bounding boxes of all UAVs, but also locations of some important parts of UAVs and locations of all pixels covered by UAVs, which can be used for more complicated application, such as mask detection or keypoint detection.
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