Deep learning-based quantification and transcriptomic profiling reveal a methyl jasmonate-mediated glandular trichome formation pathway in Cannabis sativa.

Cannabis glandular trichomes (GTs) are economically and biotechnologically important structures that have a remarkable morphology and capacity to produce, store, and secrete diverse classes of secondary metabolites. However, our understanding of the developmental changes and the underlying molecular processes involved in cannabis GT development is limited. In this study, we developed Cannabis Glandular Trichome Detection Model (CGTDM), a deep learning-based model capable of differentiating and quantifying three types of cannabis GTs with a high degree of efficiency and accuracy. By profiling at eight different time points, we captured dynamic changes in gene expression, phenotypes, and metabolic processes associated with GT development. By integrating weighted gene co-expression network analysis with CGTDM measurements, we established correlations between phenotypic variations in GT traits and the global transcriptome profiles across the developmental gradient. Notably, we identified a module containing methyl jasmonate (MeJA)-responsive genes that significantly correlated with stalked GT density and cannabinoid content during development, suggesting the existence of a MeJA-mediated GT formation pathway. Our findings were further supported by the successful promotion of GT development in cannabis through exogenous MeJA treatment. Importantly, we have identified CsMYC4 as a key transcription factor that positively regulates GT formation via MeJA signaling in cannabis. These findings provide novel tools for GT detection and counting, as well as valuable information for understanding the molecular regulatory mechanism of GT formation, which has the potential to facilitate the molecular breeding, targeted engineering, informed harvest timing, and manipulation of cannabinoid production.

Comparative Metabolomic Analysis Reveals the Differences in Nonvolatile and Volatile Metabolites and Their Quality Characteristics in Beauty Tea with Different Extents of Punctured Leaves by Tea Green Leafhopper.

The fresh leaves were processed into beauty tea from the Camellia sinensis "Jinxuan" cultivar, which were punctured by tea green leafhoppers to different extents. Low-puncturing dry tea (LPDT) exhibited a superior quality. Altogether, 101 and 129 differential metabolites, including tea polyphenols, lipids, and saccharides, were identified from the fresh leaves and dry beauty tea, respectively. Most metabolite levels increased in the fresh leaves punctured by leafhoppers, but the opposite was observed for the dry beauty tea. According to relative odor activity values (rOAVs) and partial least-squares discriminant analysis (PLS-DA), four characteristic volatiles, including linalool, geraniol, benzeneacetaldehyde, and dihydrolinalool, were selected. Mechanical injury to leaves caused by leafhoppers, watery saliva secreted by the leafhopper, and different water contents of the fresh leaves in different puncturing degrees are the possible reasons for the difference in the quality of the beauty tea with different levels of puncturing. Overall, this study identified a wide range of chemicals that are affected by the degrees of leafhopper puncturing.

Genome-wide analysis of the SMXL gene family in common bean and identification of karrikin-responsive PvSMXL2 as a negative regulator of PEG-induced drought stress.

Common bean (Phaseolus vulgaris L.) is a major legume crop worldwide, but its growth and development frequently face challenges due to abiotic stresses, particularly drought. Proper supplement of copper could mitigate the adverse effects of drought, but excessive accumulation of this metal in plants can be harmful. The suppressor of MAX2 1-like (SMXL) gene family, which plays important roles in various plant processes, including stress responses, remains poorly understood in common bean. In this study, we identified nine orthologues of SMXL genes in common bean, which are located on six chromosomes and classified into four subgroups. Basic molecular properties, including theoretical isoelectric point (PI), molecular weight (MW), grand average of hydropathicity (GVIO), gene structure, and conserved motifs were characterized, and numerous cis-elements in promoters were predicted. The expression patterns of PvSMXL genes were found to be distinct under 10% polyethylene glycol (PEG)-induced drought stress and 200 µM Cu treatments. Most PvSMXLs showed reduced expression in response to Cu treatment, whereas nearly half PvSMXLs exhibited inducible expression under drought stress. PvSMXL2, which exhibited a rapid response to karrikin 1 (KAR1), an active form of the plant growth regulators newly found in the smoke of burning plant material, was down-regulated by both PEG-induced drought and Cu stresses. Transient silencing of PvSMXL2 resulted in enhanced drought stress tolerance without conferring Cu tolerance. These findings provide valuable insights into the functions of SMXL genes in common bean under abiotic stress conditions.

Chemical and Quality Analysis of Beauty Tea Processed from Fresh Leaves of Tieguanyin Variety with Different Puncturing Degrees.

Beauty tea with special flavor can be affected by the degree of leafhopper puncturing. The present research adopted widely targeted metabolomics to analyze the characteristic metabolites of fresh tea leaves and beauty tea with different degrees of leafhopper puncturing. Low-puncturing beauty tea (LPBT) exhibited a superior quality. Altogether, 95 and 65 differential metabolites, including tea polyphenols, saccharides, and lipids, were identified from fresh leaves and beauty tea, respectively. The partial least squares regression (PLSR) analysis results showed that isomaltulose, theaflavic acid, and ellagic acid, may be the characteristic metabolites that form the different taste outlines of beauty tea. Based on odor activity values (OAVs) and partial least squares discriminant analysis (PLS-DA), dihydrolinalool and cis-linalool oxide were identified as characteristic volatile components, which may be essential for the formation of the different aroma characteristic of beauty tea. The results provide a theoretical basis for selecting raw materials, performing quality research, and developing beauty tea industrially.

Preparation and Performance Evaluation of a Self-Crosslinking Emulsion-Type Fracturing Fluid for Quasi-Dry CO2 Fracturing.

Quasi-dry CO2 fracturing technology is a new CO2 fracturing technology that combines liquid CO2 fracturing (dry CO2 fracturing) and water-based fracturing. It uses a liquid CO2 system containing a small amount of water-based fracturing fluid to carry sand, and it is characterized by sand blending at normal pressure, convenient preparation, the integrated application of resistance reduction and sand carrying, and no dedicated closed sand blender requirement. We developed a self-crosslinking emulsion-type water-based fracturing fluid (ZJL-1), which contained ionic bonds, hydrogen bonds, van der Waals forces, and hydrophobic associations, for quasi-dry CO2 fracturing, and the comprehensive properties of the ZJL-1 fracturing fluid were evaluated. The results showed that the ZJL-1 fracturing fluid had obvious viscoelastic characteristics, a heat loss rate of less than 10% at 200 °C, a good thermal stability, sufficient rheology under high temperature and high shear conditions, and a good thermal stability. The resistance reduction rate reached 70%, which demonstrates a good resistance reduction performance. Compared with conventional guar fracturing fluid, ZJL-1 can carry more sand and has a lower core damage rate. The on-site use of quasi-dry fracturing showed that optimizing the mixing ratio of liquid CO2 fracturing fluid and ZJL-1 fracturing fluid effectively enhanced oil and gas recovery. This can be used to optimize quasi-dry fracturing and can be used as a reference.

SCD: A Stacked Carton Dataset for Detection and Segmentation.

Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons and the unloading of cartons in the containers. However, there is no public large-scale carton dataset for the research community to train and evaluate the carton detection models up to now, which hinders the development of carton detection. In this article, we present a large-scale carton dataset named Stacked Carton Dataset (SCD) with the goal of advancing the state-of-the-art in carton detection. Images were collected from the Internet and several warehouses, and objects were labeled for precise localization using instance mask annotation. There were a total of 250,000 instance masks from 16,136 images. Naturally, a suite of benchmarks was established with several popular detectors and instance segmentation models. In addition, we designed a carton detector based on RetinaNet by embedding our proposed Offset Prediction between the Classification and Localization module (OPCL) and the Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality, which boosts AP by 3.1∼4.7% on SCD at the model level, while BGS guides the detector to pay more attention to the boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL for other datasets, we conducted extensive experiments on MS COCO and PASCAL VOC. The improvements in AP on MS COCO and PASCAL VOC were 1.8∼2.2% and 3.4∼4.3%, respectively.