Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees.

Field-grown peach trees are large and have a complex branch structure; therefore, detection of water deficit stress from images is challenging. We obtained large datasets of images of field-grown peach trees with continuous values of stem water potential (Ψstem) through partial secession treatment of the base of branches to change the water status of the branches. The total number of images as frames extracted from videos of branches was 23,181, 6743, and 10,752, in the training, validation, and test datasets, respectively. These datasets enabled us to precisely model water deficit stress using a deep-learning-regression model. The predicted Ψstem of frames belonging to a single branch showed a Gaussian distribution, and the coefficient of determination between the measured and predicted values of Ψstem increased to 0.927 by averaging the predicted values of the frames in each video. This method of averaging the predicted values of frames in each video can automatically eliminate noise and summarize data into the representative value of a tree and is considered to be robust for the diagnosis of water deficit stress in large field-grown peach trees with a complex branch structure.

HortNet417v1-A Deep-Learning Architecture for the Automatic Detection of Pot-Cultivated Peach Plant Water Stress.

The biggest challenge in the classification of plant water stress conditions is the similar appearance of different stress conditions. We introduce HortNet417v1 with 417 layers for rapid recognition, classification, and visualization of plant stress conditions, such as no stress, low stress, middle stress, high stress, and very high stress, in real time with higher accuracy and a lower computing condition. We evaluated the classification performance by training more than 50,632 augmented images and found that HortNet417v1 has 90.77% training, 90.52% cross validation, and 93.00% test accuracy without any overfitting issue, while other networks like Xception, ShuffleNet, and MobileNetv2 have an overfitting issue, although they achieved 100% training accuracy. This research will motivate and encourage the further use of deep learning techniques to automatically detect and classify plant stress conditions and provide farmers with the necessary information to manage irrigation practices in a timely manner.

Inhibitory Effects of Juices Prepared from Individual Vegetables on CYP3A4 Activity in Recombinant CYP3A4 and LS180 Cells.

Human intestinal absorption and drug metabolism vary to a large extent among individuals. For example, CYP3A4 activity has large individual variation that cannot be attributed to only genetic differences. Various flavonoids in vegetables, such as kaempferol and quercetin, possess inhibitory effects, and some vegetable and fruit juices have also been found to inhibit CYP3A4 activity. Therefore, differences in daily intake of flavonoid-containing vegetables may induce individual variation in intestinal bioavailability. To identify a vegetable that strongly inhibits CYP3A4, we investigated the effects of juices, prepared from individual vegetables, on CYP3A4 activity using recombinant CYP3A4 and LS180 cells in this study. Nine vegetable juices (cabbage, Japanese radish, onion, tomato, eggplant, carrot, Chinese cabbage, green pepper, and lettuce), were prepared and recombinant CYP3A4 and LS180 cells were used for evaluation of CYP3A4 activity. Metabolism to 6ß-hydroxytestosterone by recombinant CYP3A4 was strongly inhibited by cabbage, onion, and green pepper juices, and cabbage and green pepper juices significantly inhibited CYP3A4 activity in a preincubation time-dependent manner. In addition, CYP3A4 activity in LS180 cells was significantly inhibited by cabbage and onion juices. In conclusion, this study showed that juices prepared from some individual vegetables could significantly inhibit CYP3A4 activity. Therefore, variation in the daily intake of vegetables such as cabbage and onion may be one of the factors responsible for individual differences in intestinal bioavailability.

Dissection of centromeric DNA from yeast Yarrowia lipolytica and identification of protein-binding site required for plasmid transmission.

In a dimorphic yeast, Yarrowia lipolytica, replicative plasmids can be established only in the coexistence of the replication origin (ORI) and centromere (CEN) from its own chromosomal DNA. Although six CEN sequences so far isolated from this yeast exhibit no similarity with conventional CEN DNA elements from other budding yeasts, they are confined within short regions (approximately 0.2 kb) and contain various conserved sequence blocks. We surveyed here a CEN1-1 sequence on an ORI-containing plasmid by deletion and site-directed mutagenesis, and found a partial palindrome, CCTAATTTGG designated DS9, to be an essential element for high-efficiency transformation. In particular, point mutations that alter symmetry and/or length of the palindrome abrogated the activity of CEN1-1. Gel mobility shift assay of CEN1-1 DNA fragments incubated with Y. lipolytica nuclear proteins revealed four bands corresponding to protein-DNA complexes, whereas the mutations within DS9 that disabled transformation also abolished the formation of part of these complexes, depending on particular mutations. These results demonstrate that the palindrome is a binding site for specific protein(s) necessary for plasmid transmission in Y. lipolytica.

Derivation of consensus sequence for protein binding site in Yarrowia lipolytica centromere.

The transmission of replicative plasmids in the yeast Yarrowia lipolytica is mediated solely by a part of its centromere DNA, in which an essential protein binding site has been analyzed recently. Here, we extended the analysis to other minimized centromeric regions, revealing a consensus sequence of a 17- to 21-bp imperfect palindrome.