Biotechnology

FUNGAL PRODUCTION OF CATALASE FROM MANGO (Mangifera indica) AND BANANA (Musa paradisiaca) PEELS WASTE

2025-03-05T07:08:47Z

FUNGAL PRODUCTION OF CATALASE FROM MANGO (Mangifera indica) AND BANANA (Musa paradisiaca) PEELS WASTE Mohammed Abdi Kalid; (PhD) Zekeria Yusuf; (PhD) Mulugeta Desta Microbial catalases are preferred due to their economic feasibility, rapid growth, high yield, ease of product modification and optimization. Therefore the present study aimed to assess fungal production of catalase from Banana (Musa paradisiaca) and Mango (Mangifera indica) peel wastes. The experiment involved isolation, screening, biochemical characterization of catalase-producing fungal isolates from Mango and Banana peels waste, and also the optimization of factors affecting catalase activity. The result of macroscopic and microscopic examination of the fungal morphotypes cultured onto Banana and Mango peels waste indicated the growth of catalase-positive fungal isolates belonging to three genera of fungi Fusarium and Penicillium from Mango peel while only Aspergillus sp was isolated from Banana peel. Aspergillus sp has presented maximum catalase activity (39.81U/mL) at pH_9 indicating the optimum pH for catalase activity is around pH_9, and a maximum catalase activity (134.50U/mL) at 60oC. Conversely, Penicillium sp has recorded maximum catalase activity (34.50U/mL) at pH_8 indicating that the optimum enzyme activity for Penicillium sp was around pH_8, and a maximum catalase activity (75.88U/mL) at 60oC. The maximum catalase activity for Aspergillus sp (86.25U/mL) was recorded at inoculum size 4mg/mL. Similarly, Penicillium sp has recorded maximum catalase activity at inoculums size (51.75U/mL). The maximum catalase activity for Aspergillus sp (57.50 U/mL) and Penicillium sp(43.13 U/mL) was recorded during 3 days of incubation. The maximum catalase activity (207.00U/mL) was recorded for Aspergillus sp isolate and the maximum catalase production for Penicillium sp isolate was 103.5U/mL with 20g/L of starch supplement. Aspergillus sp isolate has recorded the maximum catalase activity (129.38U/mL) while Penicillium sp isolate has recorded the maximum enzyme activity (86.25U/mL) at 15g/L yeast extract.It can be concluded from the present study that Aspergillus and Penicillium spp were chosen as they showed significant catalase activity compared to other catalase-producing fungi. 59p.

POLYPHENOLIC CONTENTS AND ANTIOXIDANT PROPERTIES OF SELECTED WILD EDIBLE FRUITS, AGAMSA (Carissa edulis (Forssk.) Vahl.) AND QOLAATTII (Mimusops kummel Bruce ex A. DC.) FROM ETHIOPIA

2025-02-10T07:53:14Z

POLYPHENOLIC CONTENTS AND ANTIOXIDANT PROPERTIES OF SELECTED WILD EDIBLE FRUITS, AGAMSA (Carissa edulis (Forssk.) Vahl.) AND QOLAATTII (Mimusops kummel Bruce ex A. DC.) FROM ETHIOPIA Daniel Muleta Nurgi; (PhD) Meseret Chimdessa; (PhD) Sasikumar J.M. Plants serve as valuable sources of therapy in both traditional and modern medicine. This study aimed to evaluate the antioxidant capacities of fruit extracts of Carissa edulis (Forssk.) Vahl. and Mimusops kummel Bruce ex A. DC. Dried fruits of both plants were extracted by macerating them in three different organic and aqueous solvents to quantify their polyphenolic content using spectrophotometric technique. The antioxidant capacities of the fruit extracts were assessed through DPPH, nitric oxide and H₂O₂ scavenging, as well as ferric ion-reducing power assays. Each experiment was conducted in triplicate. Results indicated that the total phenolic and flavonoid contents varied significantly (p < 0.05) among the different solvents, with values decreasing as polarity diminished. Notably, Mimusops kummel exhibited significantly (p < 0.05) higher polyphenolic content compared to C. edulis across all solvents tested. Antioxidant activity assessments revealed significant (p < 0.05) differences based on solvent type and extract concentration in DPPH scavenging activity. Methanolic extracts of both C. edulis and M. kummel demonstrated the highest DPPH scavenging activity when compared to extracts from other solvents and ascorbic acid, a standard antioxidant. In the nitric oxide scavenging assay, significant (p < 0.05) differences were also noted, with aqueous extracts of both plants showing the highest activity. Significant (p < 0.05) differences in antioxidant capacity were observed across solvents and extract concentrations for H₂O₂ scavenging as well. Ethyl acetate extracts exhibited superior activity compared to other solvents and the standard antioxidant BHT. Ferric ion-reducing power assays also revealed significant (p < 0.05) variations, with ethyl acetate extracts of C. edulis performing better than those from other solvents, while methanolic extracts were more effective for M. kummel. Overall, Mimusops kummel consistently showed higher polyphenolic content than C. edulis, correlating with their respective antioxidant capacities. This study highlights that both C. edulis and M. kummel are rich in polyphenols and possess considerable potential for scavenging reactive oxygen species, suggesting their potential utility in preventing degenerative diseases linked to oxidative stress. 65p.

GENOME WIDE ASSOCIATION ANALYSIS OF AGRONOMIC TRAITS IN DURUM WHEAT (Triticum turgidium var. durum) FOR SEEDLING DROUGHT TOLERANCE

2024-12-31T07:08:07Z

GENOME WIDE ASSOCIATION ANALYSIS OF AGRONOMIC TRAITS IN DURUM WHEAT (Triticum turgidium var. durum) FOR SEEDLING DROUGHT TOLERANCE EMIRU CHIMDESSA GEMECHU; Dr.Kefyalew Negisho(PhD); Dr.Zekeriya Yusuf (PhD) Drought is one of the leading abiotic stresses reducing the production and productivity of durum wheat in Sub-Saharan Africa, in particular Ethiopia. Thus, genome-wide association analysis of agronomic traits associated with seedling drought tolerance provides a basis for the development of drought-tolerant cultivars in durum wheat. The study aimed to examine the effects of water stress on agronomic traits in durum wheat, asses trait correlations under varied water conditions, and detect marker trait associations (MTAs) and quantitative trait loci (QTLs) under varied water conditions at the seedling stage. A total of 150 durum wheat genotypes were evaluated in the greenhouse at the National Agricultural Biotechnology Research Center, Holeta, using a completely randomized design under well-watered and water stress conditions with three replications. Genome-wide association (GWA) analysis was conducted using 9396 SNP markers and best linear unbiased estimator values of nine agronomic traits. The analysis of variance revealed highly significant variation among genotypes for all the studied traits under both well-watered and water-stress conditions, suggesting that genotypes were genetically diverse. Broad sense heritability ranged from 44% for root length to 73% for leaf chlorophyll content under well-watered condition, and from 17% root dry weight to 71% leaf chlorophyll content under water-stress condition. Results revealed that water stress significantly reduced shoot fresh weight (SFW) (60.66%), flag leaf area (FLA) (32.37%), root fresh weight (RFW) (31.25%), shoot dry weight (SDW) (23.81%), leaf chlorophyll content (LCC) (18.55%), shoot length (SL) (15.29%), and root dry weight (RDW) (12.9%) compared with the well-watered condition, but increased root length (RL) (20.53%) and root to shoot dry weight ratio (RS) (14.85%). Pearson’s correlation analysis unveiled significant and positive associations among the majority of the studied traits in both well watered and water stress conditions, except for negative correlations observed between SL and LCC in well-watered condition, and between LCC and SL, SFW as well as between RS and SDW, SFW in water stress condition. These findings suggest that selecting for one trait may enhance the performance of another. Principal component analysis identified that the first three components, each with eigenvalues greater than 1, explained 76.38% and 62% of the total variation under well-watered and water stress conditions, respectively. Traits such as RDW, SFW, FLA, LCC, and SL significantly contributed to overall variations under both well-watered and water stress conditions. This highlights the significance of these traits as key indicators for breeding programs aimed at improving drought tolerance in durum wheat. The GWA analysis identified a total of 114 significant MTAs, comprising 54 under well-watered condition and 60 under water stress condition. Furthermore, a total of 52 QTLs were detected, with 24 under well-watered condition, and 28 under water stress condition. The identified MTAs and QTLs included previously discovered and novel loci/genomic regions associated with various studied traits. The MTAs and QTLs discovered in this study hold promise for marker-assisted selection in breeding programs aimed at developing drought-tolerant durum wheat cultivars after further validation using larger and more diverse populations. 114

SOCIAL MEDIA-BASED MULTI-CLASS CLASSIFICATION MODEL FROM AMHARIC HATE SPEECH TEXT USING DEEP LEARNING

2024-12-31T06:45:24Z

SOCIAL MEDIA-BASED MULTI-CLASS CLASSIFICATION MODEL FROM AMHARIC HATE SPEECH TEXT USING DEEP LEARNING Demelash Seifu; Dr. Temtim Assefa; Mr. Tadesse Kebede The proliferation of hate speech on social media poses significant challenges to social cohesion and stability, particularly in Ethiopia. This research investigates approaches to detecting and classifying Amharic hate speech on Facebook using deep learning techniques. To address these challenges, this study developed a multi-class hate speech detection system focusing on three critical categories: ethnic, political, and religious hate speech. Using a comprehensive dataset of 4,067 Facebook posts from pages with over 50,000 followers, the study manually categorized content into three hate speech types: ethnic (1,497 posts), political (1,320 posts), and religious (1,250 posts). The study employed two deep learning models, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (Bi-LSTM) networks, to analyze and classify hate speech. The dataset underwent meticulous preprocessing through tokenization, text cleaning, and normalization techniques to ensure data quality. This study evaluates the effectiveness of Bi-LSTM and CNN deep learning models in classifying Amharic hate speech into ethnic, political, and religious categories. The Bi-LSTM model outperformed CNN, achieving a weighted average precision and recall of 0.83 and overall accuracy of 0.83, compared to CNN's 0.80 across all metrics. While both models demonstrated strong performance, Bi-LSTM showed superior capability in capturing contextual information and maintaining consistent classification accuracy across categories. Moreover, the study highlighted potential challenges in the practical implementation of deep learning-based hate speech detection systems, such as managing code-switching, adapting to evolving language patterns, and ensuring fairness and transparency. Therefore, the study recommended that collaboration with various stakeholders is crucial for the successful implementation and continuous improvement of the system. This includes working with social media platforms, government agencies, and civil society organizations to integrate the models into content moderation pipelines and policy enforcement frameworks. 96