In vitro, sterilization is one of the key components for proceeding with plant tissue cultures. Since the effectiveness of sterilization has a direct impact on the culture's final outcomes, there is a crucial need for optimization of the sterilization process. However, compared with traditional optimizing methods, the use of computational approaches through artificial intelligence-based process modeling and optimization algorithms provides a precise optimal condition for in vitro culturing. This study aimed to optimise in vitro sterilization of grape rootstock 3309C using RSM, ANN, and genetic algorithm (GA) techniques. In this context, two output responses, namely, Clean Culture and Explant Viability, were optimised using the models developed by RSM and ANN, followed by a GA, to obtain a globally optimal solution. The most influential independent factors, such as HgCl₂, NaOCl, AgNO₃, and immersion time, were considered input variables. The significance of the developed models was investigated with statistical and non-statistical techniques and was optimised to determine the significance of selected inputs. The optimal clean culture of 91%, and the explant viability of 89% can be obtained from 1.62% NaOCl at a 13.96 min immersion time, according to MLP-NSGAII. Sensitivity analysis revealed that the clean culture and explant viability were less sensitive to AgNO₃ and more sensitive to immersion time. Results showed that the differences between the GA predicted and validation data were significant after the performance validation of predicted and optimised sterilising agents with immersion time combinations were tested. In general, GA, a potent methodology, may open the door to the development of new computational methods in plant tissue culture.

Exploring genetic diversity and identifying new germplasm from genebanks is crucial for lentil breeding programs. This research was designed to assess the performance of lentil germplasm from various genebanks. About 615 diverse Ethiopian and exotic lentil germplasm, were characterized at Deneba, Amhara Region, Ethiopia using augmented design with 12 blocks and three checks. Data were recorded on 12 qualitative and 9 quantitative traits. Chi-square, Shannon diversity index, cluster and principal component analyses were employed for data extrapolation. There was significant variation among landraces, but only in terms of early plant vigor and stem pigmentation, however there was large variation among exotic lentil germplasm across their source of origin. Landraces did not show significant variation in terms of their phenology, yield, or yield-related traits as compared with exotics. However, hierarchical K clustering grouped Ethiopian landraces with germplasm from India and ICAR80 in terms of their agronomic traits. Similarly, germplasm sourced from the International Centre for Agricultural Research in Dry Area (ICARDA), and through the Australian Centre for International Agricultural Research (ACIAR), grouped in another cluster highlighting a close relationship between these two groups. In terms of seed per pod and earliness, landraces were significantly better than exotics. However, exotic lentils specifically from ACIAR and ICARDA performed best in terms of yield and yield-related traits. In general, days to maturity, yield, branching, seed weight, number of seeds per pod, and poding contributed to the large variation between landrace and exotic lentils, important traits for selection from this diverse lentil germplasm.

Synthetic dyes are toxic and their release into the environment harms the ecosystem. Phycoremediation of synthetic dyes with acclimatized and native species has advantages over other methods. In this study, textile effluent-acclimatized microalgae species of Oscillatoria were grown in Bold’s Basal Medium (BBM), dried, powdered using sonication, and optimized the removal malachite green (MG), using the response surface methodology (RSM). The effects of algal biosorbent concentration (AC), pH, and contact time (CT) were studied with 1 g L⁻¹ MG in an aqueous solution, and the interaction model exerted significance (p < 0.001). The removal of MG was higher at alkaline pH (90% at pH 8.5) than at acidic pH (70% at pH 4). Under the optimized conditions of 1.2 g L⁻¹ AC, 8.5 pH, and 30 min CT, the MG removal was documented at 90.8% with the biosorption capacity of 757 mg g⁻¹. Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD) analysis revealed the occurrence of different electronegative functional groups, aromatic vibrations, and the crystalline nature of the biosorbent. The algal sorbent exhibited a good performance of 80.9% for the removal of the crude color in real textile effluents. This microalgal sorbent is an attractive option for promoting large-scale applications. Graphical Abstract This study used an algal (Oscillatoria sp.,) biosorbent isolated from textile effluents, and it was acclimatized to a particular effluent (synthetic dye). This biosorbent was prepared using the sonication method and sieved using a 50-µm mesh. With this novel approach of biosorbent preparation (acclimatization and sonication), this study demonstrated the maximum adsorption capacity of malachite green at 757 mg g⁻¹ biosorbent under optimized conditions (1.2 g of biosorbent, pH 8.5, and a contact time of 30 min). This algal biosorbent and preparation methods will have a huge impact on the wastewater treatment technology and possible applications at a large scale.

Understanding the extent and structure of genetic diversity within breeding populations is essential for sustaining long-term genetic gain in maize improvement programs. In this study, a panel of 2,555 maize doubled haploid (DH) lines representing diverse genetic backgrounds was genotyped using 3305 high-quality single nucleotide polymorphism (SNP) markers to assess genome-wide diversity, population structure, and relatedness. The SNPs were distributed across all ten chromosomes, with varying marker densities among genomic regions. Diversity indices revealed moderate polymorphism, with mean gene diversity (0.38) and polymorphic information content (0.30), while the minor allele frequency ranged from 0.04 to 0.50. The low observed heterozygosity (0.04) and high fixation index (0.89) confirmed the expected homozygosity of DH lines. Population structure analysis using sparse non-negative matrix factorization (sNMF) and principal coordinate analysis (PCoA) consistently identified two major genetic clusters corresponding to the established heterotic groups used in CIMMYT’s tropical maize breeding pipelines. The Analysis of Molecular Variance (AMOVA) indicated that 36% of genetic variation occurred among populations, 58% among individuals within populations, and 6% within individuals ( P = 0.001), confirming significant population differentiation and high within-group diversity. These results demonstrate that the DH panel represents a genetically diverse and well-structured population with limited relatedness among lines. The distinct clustering by heterotic group, coupled with substantial within-group variation, provides a strong foundation for genome-wide association studies, genomic selection, and allele mining for complex adaptive traits. The panel’s diversity and structure make it an invaluable genomic resource for dissecting trait architecture and accelerating genetic gain in tropical maize breeding programs targeting sub-Saharan Africa and similar environments.

Yield is a complex trait influenced by multiple components and their genetic behavior. Therefore, this study was designed to understand the complex nature of yield by uncovering relationships among traits and estimating the genetic parameters of lentil germplasm. One hundred and ninety-two lentil (Lens culinaris) germplasm samples were evaluated at Jamma and Enewari (field experiment) and at Debre Birehan agricultural research center (pot experiment) in Amhara Region, Ethiopia, in 2024 and 2025. An alpha lattice design was used for both set of experiment, and data on 12 agronomic traits were collected. Genetic parameter estimations, correlation, path, principal component and cluster analyses were performed in R. The results revealed substantial phenotypic and genetic variation among the evaluated germplasm, with high broad-sense heritability for flowering, maturity, and seed size traits, and moderate heritability for seeds per plant, plant height, harvest index, and yield. Yield exhibited a strong phenotypic correlation with harvest index (r = 0.78 in field and r = 0.95 in pots), biomass (r = 0.77 in the field and r = 0.78 in pots), seeds per plant (r = 0.42 in the field and r = 0.60 in pots), and podding (r = 0.45 in the field and r = 0.69 in pots). Similarly, genotypic correlations were high with harvest index (r = 0.83 in the field and r = 0.96 in pots), biomass (r = 0.75 in the field and r = 0.80 in pots), seeds per plant (r = 0.0.59 in the field and r = 0.58 in pots), and podding (r = 0.39 in the field and r = 0.68 in pot), and both their direct and indirect effects on yield were significantly high. Therefore, indirect selection through traits such as pods per plant, harvest index, biomass, and seeds per plant would be more effective and reliable for improving seed yield than direct selection for yield itself as this is highly affected by environmental variations.