Synthetic Studies toward 5,6,7,3',4'-Monomethoxytetrahydroxyflavones: Synthesis of Pedalitin.

During the synthetic studies toward 5,6,7,3',4'-monomethoxytetrahydroxyflavones, a concise pedalitin synthesis procedure was achieved. As previously reported, 6-hydroxy-2,3,4-trimethoxyacetophenone was prepared by Friedel-Crafts acylation of 1,4-dihydroxy-2,6-dimethoxybenzene with boron trifluoride diethyl etherate in acetic acid. When aldol condensation of 6-hydroxy-2,3,4-trimethoxyacetophenone 2b with vanillin was performed in basic conditions, it produced 2'-hydroxychalcone 3b, and, surprisingly, along with 3-hydroxyflavone 4 in a considerable amount. We propose that this oxidative cyclization is presumably due to the contribution of a quinone methide, likely to be subjected to aerobic oxidation. The chalcone was then subjected to oxidative cyclization with iodine in dimethyl sulfoxide to afford flavone 5 in good yield. To our delight, serial demethylation of the three methoxy groups at the 5-, 6-, and 3'-positions of 5 proceeded smoothly to produce pedalitin 1, under hydrogen bromide solution (30% in acetic acid). The crystal structures of 3-hydroxyflavone 4 and pedalitin tetraacetate 6 were unambiguously determined by X-ray crystallography.

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings.

BACKGROUND: Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis. RESULTS: Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity. CONCLUSIONS: AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

Comparative transcriptome analysis of persimmon somatic mutants (Diospyros kaki) identifies regulatory networks for fruit maturation and size.

Bud sports in fruit crops often result in new cultivars with unique traits, such as distinct fruit size and color, compared to their parent plants. This study investigates the phenotypic differences and gene expression patterns in Tonewase and Ohtanenashi persimmon bud sports compared to those in their parent, Hiratanenashi, based on RNA-seq data. Tonewase is characterized by early maturation, whereas Ohtanenashi is noted for its larger fruit size. Despite the importance of these traits in determining fruit quality, their molecular bases in persimmons have been understudied. We compared transcriptome-level differences during fruit development between the bud sport samples and their original cultivar. Comprehensive transcriptome analyses identified 15,814 differentially expressed genes and 26 modules via weighted gene co-expression network analysis. Certain modules exhibited unique expression patterns specific to the different cultivars during fruit development, likely contributing to the phenotypic differences observed. Specifically, M11, M16, M22, and M23 were uniquely expressed in Tonewase, whereas M13 and M24 showed distinct patterns in Ohtanenashi. By focusing on genes with distinct expression profiles, we aimed to uncover the genetic basis of cultivar-specific traits. Our findings suggest that changes in the expression of genes associated with ethylene and cell wall pathways may drive Tonewase's earlier maturation, whereas genes related to the cell cycle within the M24 module appear crucial for Ohtanenashi's larger fruit size. Additionally, ethylene and transcription factor genes within this module may contribute to the increased fruit size observed. This study elucidates the differences in transcriptomic changes during fruit development between the two bud sport samples and their original cultivar, enhancing our understanding of the genetic determinants influencing fruit size and maturation.

Non-destructive assessment of cannabis quality during drying process using hyperspectral imaging and machine learning.

Cannabis sativa L. is an industrially valuable plant known for its cannabinoids, such as cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), renowned for its therapeutic and psychoactive properties. Despite its significance, the cannabis industry has encountered difficulties in guaranteeing consistent product quality throughout the drying process. Hyperspectral imaging (HSI), combined with advanced machine learning technology, has been used to predict phytochemicals that presents a promising solution for maintaining cannabis quality control. We examined the dynamic changes in cannabinoid compositions under diverse drying conditions and developed a non-destructive method to appraise the quality of cannabis flowers using HSI and machine learning. Even when the relative weight and water content remained constant throughout the drying process, drying conditions significantly influenced the levels of CBD, THC, and their precursors. These results emphasize the importance of determining the exact drying endpoint. To develop HSI-based models for predicting cannabis quality indicators, including dryness, precursor conversion of CBD and THC, and CBD : THC ratio, we employed various spectral preprocessing methods and machine learning algorithms, including logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB). The LR model demonstrated the highest accuracy at 94.7-99.7% when used in conjunction with spectral pre-processing techniques such as multiplicative scatter correction (MSC) or Savitzky-Golay filter. We propose that the HSI-based model holds the potential to serve as a valuable tool for monitoring cannabinoid composition and determining optimal drying endpoint. This tool offers the means to achieve uniform cannabis quality and optimize the drying process in the industry.