Transcription factors KNAT3 and KNAT4 are essential for integument and ovule formation in Arabidopsis.

Integuments form important protective cell layers surrounding the developing ovules in gymno- and angiosperms. Although several genes have been shown to influence the development of integuments, the transcriptional regulatory mechanism is still poorly understood. In this work, we report that the Class II KNOTTED1-LIKE HOMEOBOX (KNOX II) transcription factors KNOTTED1-LIKE HOMEBOX GENE 3 (KNAT3) and KNAT4 regulate integument development in Arabidopsis (Arabidopsis thaliana). KNAT3 and KNAT4 were co-expressed in inflorescences and especially in young developing ovules. The loss-of-function double mutant knat3 knat4 showed an infertility phenotype, in which both inner and outer integuments of the ovule are arrested at an early stage and form an amorphous structure as in the bell1 (bel1) mutant. The expression of chimeric KNAT3- and KNAT4-EAR motif repression domain (SRDX repressors) resulted in severe seed abortion. Protein-protein interaction assays demonstrated that KNAT3 and KNAT4 interact with each other and also with INNER NO OUTER (INO), a key transcription factor required for the outer integument formation. Transcriptome analysis showed that the expression of genes related with integument development is influenced in the knat3 knat4 mutant. The knat3 knat4 mutant also had a lower indole-3-acetic acid (IAA) content, and some auxin signaling pathway genes were downregulated. Moreover, transactivation analysis indicated that KNAT3/4 and INO activate the auxin signaling gene IAA INDUCIBLE 14 (IAA14). Taken together, our study identified KNAT3 and KNAT4 as key factors in integument development in Arabidopsis.

Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study.

BACKGROUND: Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC. AIM: To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC. METHODS: This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR). RESULTS: Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%. CONCLUSION: Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.