The primary objective of this research was to ascertain the optimal circumstances for the successful growth of black chokeberry (Aronia melanocarpa (Michx.) Elliott) using tissue culture techniques. Additionally, the study aimed to explore the potential use of machine learning algorithms in this context. The present research investigated a range of in vitro parameters such as total number of roots (TNR), longest root length (LRL), average root length (ARL), number of main roots (NMR), number of siblings (NS), shoot length (SL), shoot diameter (SD), leaf width (LW), and leaf length (LL) for Aronia explants cultivated in different media (Murashige and Skoog (MS) and woody plant medium (WPM)) with different concentrations (0, 0.5, 1, 1.5, and 2 mg L⁻¹) of indole-3-butyric acid (IBA). The study showed that IBA hormone levels may affect WPM properties, affecting the LRL and ARL variables. Aronia explant media treated with 2 mg L⁻¹ IBA had the greatest TNR, NMR, NS, SL, and SD values; 31.67 pieces, 2.37 pieces, 5.25 pieces, 66.60 mm, and 2.59 mm, in that order. However, Aronia explants treated with 1 mg L⁻¹ IBA had the highest LW (9.10 mm) and LL (14.58 mm) values. Finally, Aronia explants containing 0.5 mg L⁻¹ IBA had the greatest LRL (89.10 mm) and ARL (57.57 mm) values. In general, the results observed (TNR, LRL, ARL, NMR, NS, SL, SD, LW, and LL) indicate that Aronia explants exhibit superior growth and development in WPM (25.68 pieces, 68.10 mm, 51.64 mm, 2.17 pieces, 4.33 pieces, 57.95 mm, 2.49 mm, 8.08 mm, and 14.26 mm, respectively) as opposed to MS medium (20.27 pieces, 59.92 mm, 47.25 mm, 1.83 pieces, 3.57 pieces, 49.34 mm, 2.13 mm, 6.99 mm, and 12.21 mm, respectively). In the context of the in vitro culturing of Aronia explants utilizing MS medium and WPM, an analysis of machine learning models revealed that the XGBoost and SVM models perform better than the RF, KNN, and GP models when it comes to making predictions about those variables. In particular, the XGBoost model stood out due to the fact that it had the greatest R-squared value, and showed higher predictive ability in terms of properly forecasting values in comparison to actual outcomes. The findings of a linear regression (LR) analysis were used in order to conduct an efficacy study of the XGBoost model. The LR results especially confirmed the findings for the SD, NS, and NMR variables, whose R-squared values were more than 0.7. This demonstrates the extraordinary accuracy that XGboost has in predicting these particular variables. As a consequence of this, it is anticipated that it will be beneficial to make use of the XGboost model in the dosage optimization and estimation of in vitro parameters in micropropagation studies of the Aronia plant for further scientific investigation.

The objective of this study was to comprehend the efficiency of wheat regeneration, callus induction, and DNA methylation through the application of mathematical frameworks and artificial intelligence (AI)-based models. This research aimed to explore the impact of treatments with AgNO₃ and Ag-NPs on various parameters. The study specifically concentrated on analyzing RAPD profiles and modeling regeneration parameters. The treatments and molecular findings served as input variables in the modeling process. It included the use of AgNO₃ and Ag-NPs at different concentrations (0, 2, 4, 6, and 8 mg L⁻¹). The in vitro and epigenetic characteristics were analyzed using several machine learning (ML) methods, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor classifier (KNN), and Gaussian processes classifier (GP) methods. This study’s results revealed that the highest values for callus induction (CI%) and embryogenic callus induction (EC%) occurred at a concentration of 2 mg L⁻¹ of Ag-NPs. Additionally, the regeneration efficiency (RE) parameter reached its peak at a concentration of 8 mg L⁻¹ of AgNO₃. Taking an epigenetic approach, AgNO₃ at a concentration of 2 mg L⁻¹ demonstrated the highest levels of genomic template stability (GTS), at 79.3%. There was a positive correlation seen between increased levels of AgNO₃ and DNA hypermethylation. Conversely, elevated levels of Ag-NPs were associated with DNA hypomethylation. The models were used to estimate the relationships between the input elements, including treatments, concentration, GTS rates, and Msp I and Hpa II polymorphism, and the in vitro output parameters. The findings suggested that the XGBoost model exhibited superior performance scores for callus induction (CI), as evidenced by an R² score of 51.5%, which explained the variances. Additionally, the RF model explained 71.9% of the total variance and showed superior efficacy in terms of EC%. Furthermore, the GP model, which provided the most robust statistics for RE, yielded an R² value of 52.5%, signifying its ability to account for a substantial portion of the total variance present in the data. This study exemplifies the application of various machine learning models in the cultivation of mature wheat embryos under the influence of treatments and concentrations involving AgNO₃ and Ag-NPs.

Investigating the genetic diversity and population structure of wheat germplasm is crucial for understanding the underlying variability essential for breeding programs and germplasm preservation. This research aims to contribute novel insights with respect to the genetic makeup and relationships among these wheat genotypes, shedding light on the diversity present within the Turkish wheat germplasm. In this study, iPBS-retrotransposon markers were employed to analyze 58 wheat genotypes, encompassing 54 landraces and 4 cultivars sourced from Türkiye. These markers serve as genetic indicators that can be used to evaluate genetic variation, build genealogical trees, and comprehend evolutionary connections. The PCR products were visualized on agarose gel, and bands were scored as present/absent. The ten iPBS primers collectively yielded an average of 16.3 alleles, generating a total of 163 polymorphic bands. The number of alleles produced by individual markers ranged from 4 (iPBS-2386) to 29 (iPBS-2219). The genetic parameters were calculated using the popgen and powermarker programs. The genetic relationships and population structures were assessed using the ntsys and structure programs. Polymorphism information content (PIC) per marker varied from 0.13 (iPBS-2390) to 0.29 (iPBS-2386), with an average value of 0.22. Shannon’s information index (I) was calculated as 1.48, while the number of effective alleles (Ne) and Nei’s genetic diversity (H) were determined to be 0.26 and 0.31, respectively. Genotype numbers 3 (Triticum dicoccum) and 10 (Triticum monococcum) exhibited the maximum genetic distance of 0.1292, signifying the highest genetic disparity. Population structure analysis revealed the segregation of genotypes into three distinct subpopulations. Notably, a substantial portion of genotypes clustered within populations correlated with the wheat species. This population structure result was consistent with the categorization of genotypes based on wheat species. The comprehensive assessment revealed noteworthy insights with respect to allele distribution, polymorphism content, and population differentiation, offering valuable implications for wheat breeding strategies and germplasm conservation efforts. In addition, the iPBS markers and wheat genotypes employed in this study hold significant potential for applications in wheat breeding research and germplasm preservation.