Volatile organic compounds (VOCs) play key roles in plant-plant communication, especially in response to pest attack. E-2-hexenal is an important component of VOCs, but it is unclear whether it can induce endogenous plant resistance to insects. Here, we show that E-2-hexenal activates early signaling events in Arabidopsis (Arabidopsis thaliana) mesophyll cells, including an H₂O₂ burst at the plasma membrane, the directed flow of calcium ions, and an increase in cytosolic calcium concentration. Treatment of wild-type Arabidopsis plants with E-2-hexenal increases their resistance when challenged with the diamondback moth Plutella xylostella L., and this phenomenon is largely lost in the wrky46 mutant. Mechanistically, E-2-hexenal induces the expression of WRKY46 and MYC2, and the physical interaction of their encoded proteins was verified by yeast two-hybrid, firefly luciferase complementation imaging, and in vitro pull-down assays. The WRKY46-MYC2 complex directly binds to the promoter of RBOHD to promote its expression, as demonstrated by luciferase reporter, yeast one-hybrid, chromatin immunoprecipitation, and electrophoretic mobility shift assays. This module also positively regulates the expression of E-2-hexenal-induced naringenin biosynthesis genes (TT4 and CHIL) and the accumulation of total flavonoids, thereby modulating plant tolerance to insects. Together, our results highlight an important role for the WRKY46-MYC2 module in the E-2-hexenal-induced defense response of Arabidopsis, providing new insights into the mechanisms by which VOCs trigger plant defense responses.

The MADS-box gene family encodes a type of transcription factor, and plays a key role in the growth and development of plants. Here, we identified 62 MADS-box genes in the melon ( Cucumis melo ) genome using bioinformatics methods. These genes were divided into type I Mα, Mγ, and Mδ subfamilies (26 members) and type II MIKC ^C subfamilies (36 members) by phylogenetic analysis. There were no genes in type II AGL12, BS, TM8, and MIKC* subfamilies, and type I Mβ subfamilies. Conserved motif analysis showed that all motifs had a subfamily-specific distribution except the M domain. The expression analysis of the MADS-box genes showed different expression characteristics. In summary, this study is the first to identify melon MADS-box genes and analyze their gene structures, subfamily distribution, and expression characteristics. These results provide a foundation for investigating the functions of the melon MADS-box genes.

The WRKY family of transcription factors plays important roles in plant growth and responses to biotic and abiotic stresses. Ammopiptanthus nanus, the only evergreen broadleaf shrub endemic to the desert and semi-desert regions of northwestern China, is highly tolerant to various stresses. However, a systematic study of WRKY proteins in A. nanus has not been reported. In the present study, we identified 63 WRKY genes in the A. nanus genome. Based on the conserved WRKY domains, zinc finger structures, and phylogenetic relationships in their encoded proteins, we classified these genes into four groups (group I–IV) and several subgroups (subgroup IIa–IIe). Conserved motif analysis showed that all motifs except those within the WRKY domains had a subfamily-specific distribution. Expression analysis revealed that the AnWRKY genes had distinct expression patterns, with some being more responsive to herbivory and drought stresses than others. Based on the results of our current study, we speculate that AnWRKY40 and AnWRKY48 are positive regulators of the plant’s response to drought and herbivory stresses, respectively. Our results indicate that AnWRKY genes contribute to the ability of A. nanus plants to withstand harsh, dry conditions.

Segmentation of mammographic masses is a challenging task since masses on mammograms typically have fuzzy and irregular edges. In the case of tissue adhesion, the region growing algorithm combined with maximum likelihood analysis will lead to a problem of over-segmentation. For the reason given above, an improved adaptive region growing algorithm for mass segmentation is proposed in this paper. In this algorithm, a hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis is developed. In order to accommodate different situations of masses, the likelihood and the edge gradients of segmented masses are weighted adaptively by the use of information entropy. 40 benign and 37 malignant tumors were tested in this study. Compared with conventional region growing algorithm, our proposed algorithm is more adaptive and robust, and it could obtain segmentation contour more accurately.