The silkworm, Bombyx mori,is an insect that is economically important for silk production, cosmetics, medical applications, food, and scientific research. The oviposition behavior of the female moth affects the number of eggs and the volume of silk production. This research aimed to investigate the relationship between the various treatment conditions of mulberry odor, the expression levels of Bombyx mori odorant receptor genes (BmOrs)in the antenna of female Bombyx mori moths, and the moths’ oviposition behavior. Four treatment conditions (fresh mulberry leaves, 2% mix, mulberry leaf juice, and 2% powder) showed a higher oviposition rate than that of the control. Our results revealed that the expression levels of BmOr44, BmOr54, BmOr56,and BmOr63 might play a major role in oviposition. The predicted three-dimensional structures of BmOr44, BmOr54, BmOr56, and BmOr63 proteins were found similar and some active compounds of mulberry leaf could virtually bind to these proteins. The expression patterns of BmOr19 and BmOr30,the specific female adult moth odorant receptor genes, were similar in almost every treatment.
Textile industries are the second-largest polluting sector in contaminating soil and water resources. Freshwater depletion demands the recycling and reuse of industrial wastewater. This study aimed for microbial treatment of dye-contaminated water for agricultural applications. In this study, potential microbes were isolated and screened based on a maximum dye tolerance test, decolorization, and degradation studies against four reactive textile dyes (Black B, Red GDN, Black NN, and Yellow GDBR). The potential isolate was identified as Pseudomonas aeruginosa strain VITKSS1. Decolorization of the reactive dye mix at 100 ppm, 500 ppm, and 1000 ppm was observed as 100% in 7 days, 94.4% in 8 days, and 85.9% in 10 days respectively. Bio-degraded dye metabolites were analyzed using GC-MS and FTIR. Based on the GC-MS analysis, an 87.85% reduction of Bis-(2-ethylhexyl)-phthalate (DEHP) was observed. The surface of the degraded dye metabolites was visualized in SEM. The isolate VITKSS1 showed positive results on plant growth-promoting rhizobacteria (PGPR) screening. The phytotoxicity study was performed to assure the safety of treated wastewater for irrigation. This study revealed that dye-contaminated water can be treated using VITKSS1 and reused efficiently.
Information on soil organic carbon stock (SOCs) and carbohydrates (R-CHO) in soils is a prerequisite to understanding the maintenance of soil health, because they promote aggregate stability, soil aeration and the amount of CO2 in the atmosphere. The study was carried out to quantified soil organic carbon stock and acid-soluble carbohydrates in soils under different land uses in the University of Port Harcourt Research Farms and related them to maintenance of soil structural indices. The land use types were: Teak (Tectona grandis), Gmelina (Gmelina arborea), Rubber (Hevea brasilensis), and continuously cultivated plots to maize and cassava (CC). Results revealed significant changes in mean weight diameter (MWD) of water stable aggregates, acid soluble carbohydrates, and soil organic carbon storage amongst the various land use types. Mean weight diameter of the topsoil was highest in Teak (0.93 mm), followed by 0.84 mm in Gmelina soils. Acid soluble carbohydrates (R-CHO) values were 20.67, 19.80, 18.67 and 3.60 g/kg-1 for Rubber, Gmelina, Teak and (CC) soils, respectively. Cultivation of Teak, Gmelina, and Rubber, increased topsoil organic carbon stock by 102.8, 90.2, and 60.8% respectively, compared to the CC soil. The dry bulk density varied significantly (p < 0.05) in Teak at 1.28 g cm-3 and 1.68 g cm-3 in CC soils. Saturated hydraulic conductivity (Ksat) value as slow as 4.8 cm h-1 was obtained in CC, compared to rapid Ksat values of 25.0 and 22.6 cm h-1 in Teak and Gmelina, respectively. Relationships showed a strong positive linear correlations between MWD and SOCs (r= 0.873, p < 0.01) and R-CHO (r= 0.856, p < 0.01). A positive correlation of SOCs with macro aggregates explained the involvement of SOC stock in the stabilization of micro aggregates for the formation of macro aggregates which promotes soil aeration and capillary pores, thereby, preventing soil degradation and compaction. Therefore, integrating these forest plants into the farming systems would help in improving the structural indices of the soil and also store significant quantity of SOC.
With the aquaculture industry’s growth comes an increased demand for fishmeal, leading to overfishing and rising prices. Spirulina, amicroalga rich in protein and essential nutrients, is proposed as an alternative protein source research in the diet of Nile tilapia, Oreochromis niloticus,a major aquaculture species. This study investigates the effect of replacing fishmeal with Spirulina at a higher percentage than in previous studies. The formulated diets containing 0%, 30%, and 50% Spirulina were tested over a 58-day period to determine the effect on growth, survival, and proximate composition on Nile tilapia. Results showed that replacing fishmeal with Spirulina at both 30% and 50% levels did not negatively affect growth or survival rates. Water quality parameters remained within optimal ranges throughout the experiment, indicating that Spirulina inclusion did not adversely affect water quality. Proximate analysis of the fish muscle revealed no significant differences in crude protein content but fish fed 30% Spirulina had higher crude fat content. Overall, the study concludes that Spirulina can replace fishmeal up to 50% without negative effect on the growth, survival and proximate composition of Nile tilapia.
Achieving sustainable cities and promoting responsible consumption require innovative approaches to chemical design and manufacturing. Precise prediction of chemical biodegradability is crucial for evaluating environmental concerns and facilitating the transition towards green chemistry. This study investigates the effectiveness of ten distinct groups of three-dimensional (3D) molecular descriptors for classifying compounds with rapid biodegradability. The Merck molecular force field (MMFF94s) was used to compute descriptors and generate 3D conformations for a dataset of chemical compounds. The dataset underwent rigorous preprocessing, including feature selection, outlier management, and scaling. Support Vector Machines (SVMs) were tested alongside three tree-based ensemble learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest. Bayesian optimization was employed to optimize model hyperparameters and enhance cross-validated Area Under the Receiver Operating Characteristic Curve (AUC). The GETAWAY descriptors, 3D autocorrelation descriptors, and 3D-MoRSE descriptors consistently demonstrated superior performance compared to other descriptors across all machine learning models. An SVM model trained on 3D autocorrelation descriptors achieved the highest prediction accuracy (0.88), sensitivity (0.83), specificity (0.91), F1-score (0.82), Cohen’s Kappa statistic (0.74), and an AUC of 0.93 on an independent test set. Advanced analytical techniques, including Permutation Feature Importance (PFI), SHapley Additive exPlanations(SHAP), and partial dependency plots (PDP) were utilized to identify the most influential 3D autocorrelation descriptors. The findings of this study demonstrate that 3D molecular descriptors, particularly 3D autocorrelations, play a critical role in developing accurate and interpretable models for predicting chemical biodegradability. These models contribute significantly to the advancement of green chemical design and the development of effective regulatory policies that support the objectives of SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production). By fostering sustainable chemical manufacturing practices, we can create healthier and more resilient urban environments while minimizing the environmental impact of human activities.
