PtoMPO1, a negative mediator, functions in poplar drought tolerance

Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis

Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield. Currently, high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants, such as water and nutrient content. In this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping. Four varieties of poplar saplings were cultivated, and 5 different irrigation treatments were applied. Color images of the plant samples were captured for analysis. Two tasks, including leaf posture calculation and drought stress identification, were conducted. First, instance segmentation was used to extract the regions of the leaf, petiole, and midvein. A dataset augmentation method was created for reducing manual annotation costs. The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization. Second, multitask learning models were proposed for simultaneously determining the stress level and poplar variety. The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein, respectively. Drought stress increased the horizontal angle of leaves. Moreover, using raw images as the input, the multitask MobileNet achieved the highest accuracy (99% for variety identification and 76% for stress level classification), outperforming widely used single-task deep learning models (stress level classification accuracies of <70% on the prediction dataset). The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.

Domain of unknown function (DUF) proteins in plants: function and perspective

Domains of unknown function (DUFs), which are deposited in the protein family database (Pfam), are protein domains with conserved amino acid sequences and uncharacterized functions. Proteins with the same DUF were classified as DUF families. Although DUF families are generally not essential for the survival of plants, they play roles in plant development and adaptation. Characterizing the functions of DUFs is important for deciphering biological puzzles. DUFs were generally studied through forward and reverse genetics. Some novelty approaches, especially the determination of crystal structures and interaction partners of the DUFs, should attract more attention. This review described the identification of DUF genes by genome-wide and transcriptome-wide analyses, summarized the function of DUF-containing proteins, and addressed the prospects for future studies in DUFs in plants.