With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification models learn features from data by themselves and have shown outstanding performance in computer vision tasks. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset with the ResNet-18 model and designed the ResNet-SP model with less computation, higher accuracy and stability based on the ResNet-18 model. The results show that the proposed ResNet-SP has a training time of 242 s and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 s for training with an accuracy of 79.88%.

Anthocyanins are a major class of flavonoids that are produced in the tissues of many plant species in response to environmental signals. The pigmentation of Chinese cabbage (Brassica rapa) was analyzed using newly developed 300K Brassica rapa microarrays. The significantly expressed genes were analyzed by clusters of orthologous groups (COGs) and hypergeometric analyses for transcription factors associated with anthocyanin pigmentation. The candidate genes were classified into 11 groups that exhibited functionally diverse transcription factor activity involved in anthocyanin-based pigmentation. Thirty-eight unknown genes were identified that potentially play a role in anthocyanin biosynthesis. Three of these unknown genes differed dramatically among cabbage leaves with yellow, green, or red pigmentation. These three genes may play a regulatory role in the anthocyanin production process or may be related to anthocyanin metabolism during flavonoid biosynthesis. These results show functional diversity of transcription factor families and that the biological functions of the anthocyanin-related transcription factors may be activated in a pigmentation signaling pathway in Chinese cabbage (Brassica rapa).

Therefore, 3,895 candidate genes related to anthocyanin biosynthesis in Chinese cabbage were identified using a 300K Brassica rapa microarray analysis. Gene expression during six stages of leaf developmental stages were examined in FC (green leaf) and FA (red leaf) Chinese cabbage cultivars. The 317 transcription factor genes found to be associated with anthocyanin were classified into 11 functional groups. The ratio of expression levels of each transcription factor between the two cultivars was examined during the six leaf developmental stages. A total of 14 genes were found to be expressed in all developmental stages commonly. Among these genes, 10 unknown and hypothetical genes were differentially revealed to be expressed between the two cultivars at each developmental stage, as determined by microarray analysis, and were verified by RT-PCR validation. These genes most likely play regulatory roles in either anthocyanin production or metabolism during flavonoid biosynthesis. While these genes require further validation and characterization, our results illustrate the potential usefulness of this multi-layered screening method using Chinese cabbage (Brassica rapa) microarrays.

The National Academy of Agricultural Science (NAAS) has developed a web-based database to provide characterization information in silkworm. The silkworm database has four major function menus: variety searching, characterization viewing, general information and photo gallery. It provides 321 silkworm varieties characterization information for six different regions namely, Korean, Japanese, Chinese, European, Tropical and non­classified group. Additionally, the database provides 1,132 photo images regarding life cycle of various silkworm varieties. A specific characterization information table provides accession number, variety, strain and larval marking, blood color, cocoon color, cocoon shape, egg colors, remarks and image table provides photos which consist of shape and color in the different stages of larval, egg and cocoon stages.AvailabilityThe database is available for free at http://www.naas.go.kr/silkworm/english/