Transcriptome comparison analyses in UV-B induced AsA accumulation of Lactuca sativa L.

BACKGROUND: Lettuce (Lactuca sativa L.) cultivated in facilities display low vitamin C (L-ascorbic acid (AsA)) contents which require augmentation. Although UV-B irradiation increases the accumulation of AsA in crops, processes underlying the biosynthesis as well as metabolism of AsA induced by UV-B in lettuce remain unclear. RESULTS: UV-B treatment increased the AsA content in lettuce, compared with that in the untreated control. UV-B treatment significantly increased AsA accumulation in a dose-dependent manner up until a certain dose.. Based on optimization experiments, three UV-B dose treatments, no UV-B (C), medium dose 7.2 KJ·m- 2·d- 1 (U1), and high dose 12.96 KJ·m- 2·d- 1 (U2), were selected for transcriptome sequencing (RNA-Seq) in this study. The results showed that C and U1 clustered in one category while U2 clustered in another, suggesting that the effect exerted on AsA by UV-B was dose dependent. MIOX gene in the myo-inositol pathway and APX gene in the recycling pathway in U2 were significantly different from the other two treatments, which was consistent with AsA changes seen in the three treatments, indicating that AsA accumulation caused by UV-B may be associated with these two genes in lettuce. UVR8 and HY5 were not significantly different expressed under UV-B irradiation, however, the genes involved in plant growth hormones and defence hormones significantly decreased and increased in U2, respectively, suggesting that high UV-B dose may regulate photomorphogenesis and response to stress via hormone regulatory pathways, although such regulation was independent of the UVR8 pathway. CONCLUSIONS: Our results demonstrated that studying the application of UV-B irradiation may enhance our understanding of the response of plant growth and AsA metabolism-related genes to UV-B stress, with particular reference to lettuce.

Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice.

Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.