Biology subjects

Takimoto, H.

Publications and source records attributed to Takimoto, H..

2 recordsLinked to original sources

bioRxiv2021

Artificial mimicry of seasonal transcriptome dynamics in Arabidopsis thaliana reveals short- and long-term responses to environmental conditions

Plants must respond to various seasonally changing environmental stimuli. In a previous study, seasonally oscillating genes were identified by a massive time-series transcriptome analysis for the wild population of Arabidopsis halleri ssp. gemmifera, a sister species to Arabidopsis thaliana. It was not clear how environmental stimuli shaped the seasonal expression pattern of these seasonally oscillating genes. In this study we show that responses in different time-scales contributed to the formation of seasonal expression patterns for several genes. To analyze the seasonally oscillating genes, we established an experimental system to mimic seasonal expression trends using A. thaliana and a "smart growth chamber mini," a hand-made low-cost small chamber. Arabidopsis thaliana plants were cultured under conditions that mimicked the average monthly temperatures and daylengths under different day-scale incubation. In total, the seasonal trends of 1627 seasonally oscillating genes were mimicked, and they showed varying temporal responses (constant, transient, and incremental) to environmental stimuli. Our results suggest that plants perceive and integrate information regarding environmental stimuli in the field by combining seasonally oscillating genes with different temporal responsiveness.

plant biology↗

bioRxiv2021

Using a two-stage convolutional neural network to rapidly identify tiny herbivorous beetles in the field

Recently, deep convolutional neural networks (CNN) have been adopted to help beginners identify insect species from field images. However, the application of these methods on the identification of tiny congeneric species moving across heterogeneous background remains difficult. To enable rapid and automatic identification in the field, we customized a method involving real-time object detection of two Phyllotreta beetles. We first performed data augmentation using transformations, syntheses, and random erasing of the original images. We then proposed a two-stage method for the detection and identification of small insects based on CNN, where YOLOv4 and EfficientNet were used as a region proposal network and a re-identification method, respectively. Evaluation of the model revealed that one-step object detection by YOLOv4 alone was not precise (Precision = 0.55) when classifying two species of flea beetles and background objects. In contrast, the two-step CNNs improved the precision (Precision = 0.89) with moderate accuracy (F-measure = 0.55) and acceptable speed (ca. 5 frames per second for full HD images) of detection and identification of insect species in the field. Although real-time identification of tiny insects remains a challenge in the field, our method aids in improving small object detection on a heterogeneous background.

ecology↗