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Laural ( ) is a Mediterranean plant with reported antibacterial properties, yet the genetic basis of its antibacterial efficacy remains largely unexplored. This study evaluated the antibacterial activity of methanolic extracts against , , and , combined with genome-wide association studies (GWAS) and machine learning (ML) approaches to identify genetic markers and predict antibacterial efficacy in 92 plant samples. Antibacterial tests revealed significant variability in inhibition zones, with showing the highest inhibition (Canakkale2: 24.5 mm), followed by (Aydin2: 26.0 mm). Minimum inhibitory concentration (MIC) analysis demonstrated notable regional differences; extracts from Mersin3 showed the highest efficacy (MIC = 6.25 mg/mL), while Aydin1 exhibited the lowest activity (MIC = 100 mg/mL). Population structure and neighbor joining tree analysis split the germplasm into two groups. GWAS identified significant genetic markers associated with antibacterial traits, including marker 26557159 for EC-MEAN ( -Mean) ( = 1.10 × 10 , MarkerR = 0.1799, genetic variance = 9.41792) and marker 26584774 for BC-MEAN ( -Mean) ( = 8.89 × 10 , MarkerR = 0.18512, genetic variance = 12.48948). Protein-protein interaction network of loci associated with marker trait association (MTA) marker (26557159) indicated involvement in high-affinity secondary active ammonium transmembrane transporter activity, providing insights into genetic regions influencing antibacterial properties. ML models predicted antibacterial activity with high accuracy. XGBoost achieved the best performance for MIC predictions (R = 0.999, RMSE = 0.434), while random forest (R = 0.984) demonstrated robust performance for both MIC and disc diffusion assays. LightGBM performed well for MIC prediction (R = 0.988) but showed limited accuracy for disc diffusion outcomes (R = 0.695). This study is the first to combine GWAS and ML for predicting antibacterial efficacy in , identifying specific genetic markers (e.g., 26557159, 26584774) and demonstrating that XGBoost achieves near-perfect MIC prediction (R = 0.999). These findings provide a genomic and computational foundation for marker-assisted breeding of laurel with enhanced antibacterial properties and support the sustainable use of plant-derived anti-microbials.

Bread wheat (Triticum aestivum L.) is a primary staple crop in Uzbekistan, where soil salinization and water scarcity significantly constrain agricultural productivity. Identifying salt-tolerant germplasm through molecular and phenotypic screening is essential for developing resilient varieties. In this study, nine bread wheat varieties were evaluated using a completely randomized design under two treatments: control (distilled water) and salt stress (200 mM NaCl). Phenotypic assessment focused on seedling traits, including Germination Rate Index (GRI), Shoot Fresh Weight (SFW), Root Fresh Weight (RFW), Shoot Dry Weight (SDW), Root Dry Weight (RDW), and the mean Salt Tolerance Trait Index (STTI). Molecular diversity was assessed using five decamer Random Amplified Polymorphic DNA (RAPD) primers. A total of 19 polymorphic loci were amplified, showing 100% polymorphism across all markers. The primer OPC-06 exhibited the highest informativeness with a Polymorphism Information Content (PIC) of 0.86, while the average PIC across all primers was 0.64. Under 200 mM NaCl stress, the mean STTI value calculated as an average across all investigated morphological traits was 82.90%, with significant genotypic variation (p < 0.01) observed. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) cluster analysis divided the genotypes into three distinct clusters, where the variety Pakhlavon formed a genetically distinct lineage. These results demonstrate that the integration of RAPD markers and phenotypic STTI screening effectively identifies salt-tolerant genotypes. Specifically, local varieties Pakhlavon and Ok Marvarid were identified as superior genetic resources for future breeding programs aimed at improving wheat resilience in salinity-prone environments.

This study investigates the physiological and biochemical properties of different sweet potato (Ipomoea batatas (L.) Lam.) varieties grown in the Samarkand soil-climatic conditions and assesses the impact of these varieties on soil enzyme activities. The red, yellow, and white varieties were evaluated for their carbohydrate content, vitamin levels, antioxidant activities, growth parameters, root system development, and enzyme activities, including protease, amylase, and cellulase. Biochemical analysis revealed that the red variety had the highest carbohydrate content (25%) with significant levels of sucrose, glucose, and fructose. It also exhibited the highest vitamin C (30 mg/100g) and vitamin A (8 mg/100g) concentrations as determined by High-Performance Liquid Chromatography (HPLC). Antioxidant activity, measured using the DPPH assay, was highest in the red variety (85% scavenging activity). Physiological analysis showed that the red variety had superior growth parameters with a height of 35 cm, root length of 20 cm, and biomass of 150 g. It also demonstrated the most developed root system with a root weight of 50 g and root volume of 40 cm³. Enzyme activity assays indicated that the red variety had the highest levels of protease (85 U/mg), amylase (70 U/mg), and cellulase (65 U/mg). The study highlights the significant variations among sweet potato varieties in terms of their nutritional and health-promoting characteristics. The red variety emerged as the most beneficial, exhibiting superior nutritional content, antioxidant activity, and enzyme activities that enhance soil health. These findings underscore the potential of red sweet potatoes in promoting sustainable agriculture and improving food security in regions with challenging environmental conditions.

Low temperature is an abiotic stress factor that affects plant development and geographic expansion, resulting in significant economic losses in worldwide food production each year. Through evolution, plants have developed intricate adaptation systems, with epigenetic regulation—modifying gene expression without altering DNA sequences—being pivotal in the cold stress response and memory formation. This review provides a systematic overview of the role of the main epigenetic mechanisms, including histone modifications, DNA methylation, non-coding RNA regulation, and chromatin remodeling, in plant cold stress responses. The article summarized research on plants, including model species Arabidopsis thaliana and rice, examined epigenetically mediated cold-stress memory and transgenerational epigenetic inheritance, and analyzed synergistic interactions among regulatory pathways. Finally, by integrating recent advances, the study identifies scientific challenges and research bottlenecks in this field and outlines future research directions and application prospects in cold-resistant crop breeding. Finally, by integrating recent advances, the study identifies scientific challenges and research bottlenecks in this field, outlines future research directions and application prospects for cold-resistant crop breeding, and provides references for further elucidating the molecular mechanisms of plant cold adaptation and for developing new cold-resistant crop varieties. These outcomes offer references for further elucidating the molecular mechanisms of plant cold adaptation and developing new cold-resistant crop varieties.

Strawberry (Fragaria × ananassa Duch.) is a widely cultivated and economically important fruit crop with increasing consumer demand worldwide. Nowadays, in Uzbekistan, strawberry cultivation surpasses that of many other fruits and vegetables in terms of production volume. However, most genetic studies have focused on a limited set of cultivars, leaving a substantial portion of varietal diversity unexplored. This study aimed to evaluate the genetic variability and heritability among selected strawberry cultivars, as well as correlations between certain valuable agronomic traits, using molecular and statistical approaches. Polymorphism analysis was performed, using 67 gene-specific SSR markers, through PCR, and allele variations were observed in 46.3% of the markers analyzed. Among them, 31 markers displayed polymorphic bands, identifying fifty alleles, with one to four alleles per marker. Phylogenetic analysis was performed using MEGA 11 software, while statistical evaluations included AMOVA (GenAIEx), correlation (OriginPro), and descriptive statistics based on standard agronomic methods. Additionally, the degree of cross-compatibility and pollen viability among the cultivars were studied, and their significance for cultivar hybridization was analyzed. The highest fruit weight was observed in the Cinderella cultivar (26.2 g), and a moderate negative correlation (r = −0.688) was found between fruit number and fruit weight. These findings demonstrate the potential of molecular tools for assessing genetic diversity and provide valuable insights for breeding programs aimed at developing improved strawberry cultivars with desirable agronomic traits.