Recently, an interest has surged in incorporating extracts of medicinal plants as active ingredients in cosmetic formulations, highlighting the need to analyze medicinal plants of cosmetic interest for phytochemicals and bioactivities. The tuber of the Ethiopian medicinal plant Impatiens tinctoria A. Rich (Ensosila) is used to make traditional cosmetics. The aim of the study was to investigate the antioxidant and mineral content of the Impatiens tinctoria tuber. Water, ethanol, and methanol were used during the extraction process. High phenolic content was found in methanol extract (107.8 ± 0.025 µg/ml GAE) followed by water extract (92.4 ± 0.02 µg/ml GAE). High flavonoid content was also obtained in methanol extract (136.7 ± 0.04 µg/ml QE). Strong 2,2-diphenyl-1-picryl-hydrazyl (DPPH) scavenging activity was recorded for methanol extract with IC50 value of 44.4 µg/ml, compared with ethanol extract (97.54 µg/ml) and water extracts (98.24 µg/ml). The lower IC50 value of methanolic extract demonstrated strong antioxidant activity. The three elements that were most prevalent in Impatiens tinctoria tuber out of the eight elements examined were K (170 ± 0.05 mg/100 g sample), Ca (87 ± 0.08 mg/100 g sample), and Mg (16 ± 0.01 mg/100 g sample). The phenolics, flavonoids, and minerals found in Impatiens tinctoria A. Rich (Ensosila) tuber may protect against oxidative stress-related skin damage and thus deserving attention for future applications in cosmetics formulations.

Many agronomic traits, such as grain yield, are controlled by polygenes with minor effects and epistatic interaction. Genomic selection (GS) uses genome-wide markers to predict a genomic estimate of breeding value (GEBV) that is used to select favorable individuals. GS involves three essential steps: prediction model training, prediction of breeding value, and selection of favorable individual based on the predicted GEBV. Prediction accuracies were evaluated using either correlation between GEBV (predicted) and empirically estimated (observed) value or cross-validation technique. Factors such as marker diversity and density, size and composition of training population, number of QTL, and heritability affect GS accuracies. GS has got potential applications in hybrid breeding, germplasm enhancement, and yield-related breeding programs. Therefore, GS is promising strategy for rapid improvement of genetic gain per unit time for quantitative traits with low heritability in breeding programs.

The production of nanocellulose using microbes has been gained interest across wide range of industries due to its flexible characteristics and advantages as compared to plant cellulose. The most active producers of bacterial nanocellulose were acetobacteraceae. The present study was conducted with the aim to isolate and characterize bacterial nanocellulose for the production of nanocellulose based hydrogels for removal of tetracycline from wastewater. The bacterial nanocellulose producing bacterias were isolated and screened from Awash winery waste (Ethiopia) using Hestrin-Schramm media. Analysis of the 16S rRNA gene sequence revealed that the bacterial isolate W7 shows 98.12% similarity with Bacillus subtilis strain T13 and isolate W4 shows 99.93% similarity with Bacillus subtilis strain 9-1-2.The highest bacterial nanocellulose yield of 5.15 ± 0.13 g/L was obtained from bacterial isolate W7 in Hestrin-Schramm culture media and the yield was further improved to 5.88 ± 0.25 g/L by using molasses as a main carbon source. The bacterial nanocellulose hydrogel was synthesised from bacterial nanocellulose by using glutaraldehyde as cross-linking agent. The synthesised bacterial nanocellulose based-hydrogel was tested for its tetracycline removal efficiency using a batch adsorption method at different experimental conditions. The maximum adsorption capacity (q_e) and removal efficiency (R%) was observed in the experimental conditions of pH 7 (59.52 ± 1.3 mg/g and 59.52 ± 1.3%), 60min contact time(54.97 ± 0.57 mg/g and 54.97 ± 1.5%), 50 mg adsorbent dose (128.39 ± 1.3 mg/g and 64.19 ± 1.30%), and 50 mg/L adsorption concentration (75.24 ± 1.09 mg/g and 56.69 ± 0.9%). Pore-filling effect, hydrogen bonding, hydrophilic, electrostatic and π–π interactions attributed to the adsorption of tetracycline. The result indicated that cellulose from Bacillus subtilis strain W7 is a promising adsorbent for the removal of tetracycline from waste water.

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.

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