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The fungal pathogen Puccinia striiformis f. sp. tritici, which causes yellow rust disease, poses a significant economic threat to wheat production not only in Uzbekistan but also globally, leading to substantial reductions in grain yield. This study aimed to develop yellow rust-resistance wheat lines by introgressing Yr10 and Yr15 genes into high-yielding cultivar Grom using the marker-assisted backcrossing (MABC) method. Grom was crossed with donor genotypes Yr10/6*Avocet S and Yr15/6*Avocet S, resulting in the development of F1 generations. In the following years, the F1 hybrids were advanced to the BC2F1 and BC2F2 generations using the MABC approach. Foreground and background selection using microsatellite markers (Xpsp3000 and Barc008) were employed to identify homozygous Yr10- and Yr15-containing genotypes. The resulting BC2F2 lines, designated as Grom-Yr10 and Grom-Yr15, retained key agronomic traits of the recurrent parent cv. Grom, such as spike length (13.0–11.9 cm) and spike weight (3.23–2.92 g). Under artificial infection conditions, the selected lines showed complete resistance to yellow rust (infection type 0). The most promising BC2F2 plants were subsequently advanced to homozygous BC2F3 lines harboring the introgressed resistance genes through marker-assisted selection. This study demonstrates the effectiveness of integrating molecular marker-assisted selection with conventional breeding methods to enhance disease resistance while preserving high-yielding traits. The newly developed lines offer valuable material for future wheat improvement and contribute to sustainable agriculture and food security.

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 explores the potential of wild cotton species Gossypium anomalum and Gossypium herbaceum as sources of valuable genes for improving the resistance of Gossypium hirsutum L. Ravnak−1 to drought and pests. We created synthetic and complex hybrids and evaluated them under controlled and field conditions. Drought resistance was assessed using osmotic stress with polyethylene glycol (PEG−6000). Hybrids showed significantly higher germination rate and better growth under strong stress (20% PEG) compared to the susceptible Ravnak−1 variety. Field observations confirmed that Ravnak−1 was highly susceptible to whiteflies, aphids, and spider mites, while certain synthetic hybrids demonstrated resistance. Molecular analysis using SSR markers confirmed hybridization and the inheritance of key stress-related alleles, highlighting the genetic distance between the cultivated variety and the wild species. The new hybrids show promise for developing climate-resilient cotton varieties.

Expanding genetic variability of cultivated cotton (Gossypium hirsutum) is essential for improving fiber quality and pest resistance. This study synthesized allotetraploids through interspecific hybridization between G. herbaceum (A1) and G. nelsonii (G3). Upon chromosome doubling using 0.2% colchicine, fertile F1C allotetraploids (A1A1G3G3) were developed. Cytogenetic analysis confirmed chromosome stability of synthetic allotetraploids, and 74 polymorphic SSR markers verified hybridity and parental contributions. The F1C hybrids exhibited enhanced resistance to cotton aphids (Aphis gossypii) and whiteflies (Aleyrodidae), with respective infestation rates of 5.2–5.6% and 5.4–5.8%, lower than those of G. hirsutum cv. Ravnak-1 (22.1% and 23.9%). Superior fiber length (25.0–26.0 mm) was observed in complex hybrids and backcross progeny, confirming the potential for trait introgression into elite cultivars. Phylogenetic analysis based on SSR data clearly differentiated G. herbaceum from Australian wild species, demonstrating successful bridging of divergent genomes. The F1C hybrids consistently expressed dominant G. nelsonii-derived traits regardless of the hybridization direction and clustered phylogenetically closer to the wild parent. These synthetic allotetraploids could broaden the genetic base of G. hirsutum, addressing cultivation constraints through improved biotic stress resilience and fiber quality traits. The study establishes a robust framework for utilizing wild Gossypium species to overcome genetic bottlenecks in conventional cotton breeding programs.

Enhancing plants’ resistance against FW is crucial for ensuring a sustainable global chickpea production. The present study focuses on the identification of fungal pathogens and the assessment of ninety-six chickpea samples for Fusarium wilt from the International Center for Agricultural Research in the Dry Areas (ICARDA)’s collection. Eight fungal isolates were recovered from the symptomatic chickpeas. Polyphasic identification was conducted by comparing the internal transcribed spacer region (ITS), the elongation factor 1-α (tef1-α), and beta-tubulin (tub2). Among them, Neocosmospora solani, N. nelsonii, N. falciformis, N. brevis, Fusarium brachygibbosum, and F. gossypinum were identified. An analysis of the genetic diversity of chickpeas, using 69 polymorphic simple sequence repeat (SSR) markers, revealed a total of 191 alleles across all markers, with, on average, each SSR marker detecting approximately 2.8 alleles. A STRUCTURE analysis delineated lines into two distinct sub-groups (K = 2). Association mapping, using the generalized linear model (GLM) and mixed linear model (MLM) approaches, identified six and five marker–trait associations (MTAs) for FW resistance, respectively. Notably, these TA42, TA125 (A) and TA125 (B), TA37, and TAASH MTAs, commonly found in both models, emerge as potential candidates for the targeted enhancement of FW resistance in chickpeas. To our knowledge, this study represents an inaugural report on the association mapping of genomic loci governing FW resistance in chickpeas from the ICARDA’s accessions.

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.

Marker-assisted selection (MAS) accelerates conventional breeding and enables precise evaluation of target traits. Gene pyramiding is an effective strategy for combining multiple favorable genes to improve plant performance. In this study, genes associated with high fiber strength, salt stress tolerance, and increased yield were integrated into a single genotype using a gene pyramiding approach. Seven Gossypium hirsutum cultivars/lines were used to develop complex F2 populations, which were evaluated using phenotypic and molecular approaches. Salt tolerance was assessed during germination and early seedling stages under 150 and 200 mM NaCl treatments. Fiber quality traits were determined using the HVI system, while yield potential was estimated by lint percentage. Molecular analysis with 160 SSR markers, of which 55 were polymorphic, detected 152 alleles, revealing considerable genetic diversity (2.76 alleles per locus; PIC = 0.37–0.66; He = 0.41–0.66). Promising F2 individuals combining desirable traits were identified, exhibiting fiber strength of 34.4–37.4 g/tex, lint percentage up to 39.05%, and stable performance under salt stress. These findings demonstrate that MAS combined with gene pyramiding can effectively integrate favorable alleles for fiber quality, yield, and salinity tolerance from three parental genotypes into a single genetic background, providing a promising strategy for cotton improvement.

Gossypium mustelinum, a rare wild cotton from Brazil, offers potential as a genetic resource for improving cultivated cottons. This study assessed its relationship with other cotton species via interspecific hybridization and genetic analysis. A total of 22 F1 hybrids were generated, exhibiting varied levels of compatibility. The var. marie-galante × G. mustelinum cross showed the highest seed set (87.2%), indicating good cross-compatibility. The F1 and F2 hybrids revealed strong potential for enhancing fiber traits. For instance, the cv. ‘Beshkahramon’ × G. mustelinum F1 hybrid had high heterosis (hp = 33.00) for fiber length. Its F2 population showed a mean fiber length of 36.2 mm (h2 = 0.62) and fiber yield of 41.2% (h2 = 0.61). SSR marker-based phylogenetic analysis confirmed a close relationship between G. mustelinum and two G. hirsutum subspecies: paniculatum (GD = 0.13) and var. marie-galante (GD = 0.17). The close relationship underscores the breeding potential of G. mustelinum.

Artificial intelligence is an influential technological paradigm that is quickly and comprehensively transforming financial decision-making systems worldwide. This study aims to focus on computational and economic implications of computational linguistics techniques and to enrich the existing literature in AI-driven investment modeling. Empirically, we draw on a cross-sectional research project, assessing the predictive power of linguistic data processing algorithms and the experiences of financial analysts in cost structuring contexts. A regression-based model consisting of semantic, syntactic, and pragmatic dimensions was created and estimated through correlation and multivariate regression analysis. The findings indicate that natural language processing efficiency and machine learning advancements have significant impacts on investment planning accuracy. Computational linguistics is deeply embedded in algorithmic financial models and reshapes cost forecasting frameworks through data-driven semantic interpretation. Closing the research gaps would contribute to the development of much-needed intelligent financial infrastructures. AI-enabled linguistics modeling promotes scalable optimization and context-aware applications of financial analytics, and realizes cost transparency improvements in automated investment systems. This paper offers computational insights and reflections to provide financial analysts with key information to best apply linguistically driven AI tools while being aware of contextual modeling constraints. This future research agenda provides ample scope for future empirical investigation and interdisciplinary science on semantic modeling, cost structuring, investment forecasting, and automated financial systems.