Lead toxicity regulation via protein degradation and tetrapyrrole biosynthesis pathways in Brassica species: A comparative quantitative analysis of proteomic study.

Understanding the heavy metals (HMs) tolerance mechanism is crucial for improving plant growth in metal-contaminated soil. In order to evaluate the lead (Pb) tolerance mechanism in Brassica species, a comparative proteomic study was used. Thirteen-day-old seedlings of B. juncea and B. napus were treated with different Pb(NO3)2 concentrations at 0, 3, 30, and 300 mg/L. Under 300 mg/L Pb(NO3)2 concentration, B. napus growth was significantly decreased, while B. juncea maintained normal growth similar to the control. The Pb accumulation was also higher in B. napus root and shoot compared to B. juncea. Gel-free proteomic analysis of roots revealed a total of 68 and 37 differentially abundant proteins (DAPs) in B. juncea and B. napus-specifically, after 300 mg/L Pb exposure. The majority of these proteins are associated with protein degradation, cellular respiration, and enzyme classification. The upregulated RPT2 and tetrapyrrole biosynthesis pathway-associated proteins maintain the cellular homeostasis and photosynthetic rate in B. juncea. Among the 55 common DAPs, S-adenosyl methionine and TCA cycle proteins were upregulated in B. juncea and down-regulated in B. napus after Pb exposure. Furthermore, higher oxidative stress also reduced the antioxidant enzyme activity in B. napus. The current finding suggests that B. juncea is more Pb tolerant than B. napus, possibly due to the upregulation of proteins involved in protein recycling, degradation, and tetrapyrrole biosynthesis pathway.

Exogenously applied gibberellic acid and benzylamine modulate growth and chemical constituents of dwarf schefflera: a stepwise regression analysis.

Ornamental foliage plants that have a dense appearance are highly valued. One way to achieve this is by using plant growth regulators as a tool for plant growth management. In a greenhouse with a mist irrigation system, a study was conducted on dwarf schefflera, an ornamental foliage plant, which was exposed to foliar application of gibberellic acid and benzyladenine hormones. The hormones were sprayed on dwarf schefflera leaves at 0, 100, and 200 mg/l concentrations, at 15-day intervals in three stages. The experiment was conducted as a factorial based on a completely randomized design, with four replicates. The combination of gibberellic acid and benzyladenine at 200 mg/l concentration had a significant effect on leaf number, leaf area, and plant height. The treatment also resulted in the highest content of photosynthetic pigments. Furthermore, the highest soluble carbohydrate to reducing sugars ratio was observed in treatments of 100 and 200 mg/l benzyladenine, and 200 mg/l gibberellic acid + benzyladenine. Stepwise regression analysis showed that root volume was the first variable to enter the model, explaining 44% of variations. The next variable was root fresh weight, and the two-variable model explained 63% of variations in leaf number. The greatest positive effect on leaf number was related to root fresh weight (0.43), which had a positive correlation with leaf number (0.47). The results showed that 200 mg/l concentration of gibberellic acid and benzyladenine significantly improved morphological growth, chlorophyll and carotenoid synthesis, and reducing sugar and soluble carbohydrate contents in dwarf schefflera.

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests.

Castor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The traditional method of identifying the correct category of pests required a significant amount of time and expertise. To solve this issue, automatic insect pest detection methods combined with precision agriculture can help farmers in providing adequate support for sustainable agriculture development. For accurate predictions, the recognition system requires a sufficient amount of data from a real-world situation, which is not always available. In this regard, data augmentation is a popular technique used for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. This paper proposes a hybrid manipulation-based approach for data augmentation to solve the issue of the lack of a suitable dataset for effective vision-based model training. The deep convolutional neural networks VGG16, VGG19, and ResNet50 are then adopted to analyze the effects of the proposed augmentation method. The prediction results show that the proposed method addresses the challenges associated with adequate dataset size and significantly improves overall performance when compared to previous methods.

Morphological diversity, phenolic acids, and antioxidant properties in eryngo (Eryngium caucasicum Trautv): Selection of superior populations for agri-food industry.

Eryngo (Eryngium caucasicum Trautv) a widespread species of the Apiaceae reveals high nutritional value and therapeutic properties due to the significant content of biologically active metabolites such as essential oils, phenolic compounds, and flavonoids. The present study was performed to evaluate the morphological and biochemical variability and antioxidant properties of naturally grown populations of eryngo. One-way ANOVA showed significant (p < .01) differences in the majority of parameters measured among the studied populations. The range of fresh weight was from 1.3 to 12.0 g/plant, while dry weight varied from 0.01 to 6.0 g/plant. The highest variation was observed for essential oil yield (CV = 205.32%) followed by essential oil content (CV = 126.23%) and chicoric acid content (CV = 71.18%). Total phenolics content varied from 8.85 to 88.15 mg GAE/g extract. Total flavonoids value ranged from 5.41 to 134.40 mg QE/g extract. Rosmarinic acid and chicoric acid contents varied from 0.118-1.234 and 0.014-0.597 µg/g DW, respectively. DPPH free radical scavenging activity varied from 76.12 to 513.5 µg/mL, while it ranged from 156.7 to 477.1 µg/mL with the ferrous ions (Fe2+) chelating assay. Rosmarinic acid and chicoric acid showed a significant and positive correlation (r 0.01 = 0.81 and r 0.05 = 0.40) with total phenolics, respectively. The Ward dendrogram analysis revealed two different clusters based on the parameters measured, confirming high morpho-phytochemical variability among the individuals and populations. Principal component analysis (PCA) revealed eight PCs which contributed to 99.97% of the overall variance, and leaf length, essential oil content, and antioxidant activity in terms of DPPH and Fe2+ chelating techniques were the most effective attributes for characterizing and selecting the studied population. Based on the traits related to vegetative yield and antioxidant properties, eight individuals from two populations were superior for breeding and/or farming programs.