Annual Research & Review in Biology

Aims: This study aimed to evaluate the antagonist effect of the bacteria Bacillus megaterium against the toxigenic fungus Aspergillus flavus using different methods.

Methodology: According to the method described by Vincent et al. (1991) and Arras (1993) we have determined the effect of B. megaterium on the growth of A. flavus using fungal disc and fungus spore respectively. In order to see the effect of cell free supernatant of B. megaterium on the dry mass of A. flavus, culture bacteria of 5 days old was centrifuged; filtered and cell-free supernatant was incubated with 5 mm circular plug of A. flavus. After incubation at 28°C for 9 days. The dry mass was determined by weighting every 72 h and compared with the control. Elucidation of antagonistic mechanism of B. megaterium was examined using the following tests: Hydrogen Cyanide production, production of Ammonia (NH₃) and production of extracellular enzymes such: Protease, Chitinase and Amylase.     

Results: The results showed that B. megaterium is an antagonistic bacterium that has been shown high effectiveness against the fungus A. flavus isolated form poultry feeds in Algeria. Results indicated an almost entirely decrease (47.56%) of mycelial growth using fungal disc and (40.75%) using fungus spore. In cell-free supernatant in vitro assay, B.megaterium showed significant inhibitory activity against  A. flavus when the  dry mass of mycelium decrease from 1.25  g to 0.83 g compared with the control.

Conclusion: This research shows that B. megaterium is quite important and effective as biocontrol agent against the toxigenic mold A. flavus in poultry feeds. This inhibition action is probably due to the synergistic effects of the factors such as the production of antibiotics and the extracellular enzymes such as protease, cellulase, chitinase and amylase.

This study was conducted to investigate the relationship between heterosis and gene effects estimated by the generation mean analysis. Nine traits with 74 cases of combinations cross-sit and cross- abiotic or biotic stress levels were assessed in three crops (durum wheat, pepper, and oat) and evaluated by lines crosses analysis. Trait performances of the F1 hybrid showed evident mid-parent heterosis varying from 0.6% to 89% for the 74 cases investigated. Results of Generation mean analysis revealed that the additive-dominance model was demonstrated adequate in 7 cases. Therefore the epistatic model was found appropriate in 67 cases. Analysis of correlations between gene effects estimated by the generation mean analysis revealed that heterosis was not correlated to additive, dominance or epistasis effects. Therefore, the majority of geneticists considered the non-additives effects as the genetic basis of heterosis. Thus, the lower correlations obtained between heterosis and non-additives effects were due to the bias of the classical approach’s models of genetic quantitative. In fact, many assumptions were proposed to develop this model. To conclude, non-additives results are apparently of great importance in the inheritance of quantitative traits and their roles in the heterosis expression are not to discuss. However, the quantitative genetic interpretation of mid-parent heterosis as a function of genetic effects was not possible basing on the model of line crosses analyses.

Presence of G×E interaction reduces the correlation between genotypic and phenotypic parameters and complicates progress of selection. Among several methods proposed for evaluation of the GE interaction, the AMMI and GGE-biplot are the most informative models. The objective of this study was to estimate the G×E interaction in sorghum parental lines and to identify sorghum B-lines of stability and adaptability across different environments using the AMMI and GGE-biplot models. Six environments with 25 sorghum B-lines were conducted at two locations in Egypt (Giza and Shandaweel) in two years and two planting dates in one location (Giza). A randomized complete block design was used in each environment (yield trial) with three replications. The AMMI analysis of variance indicated that the genotype (G), environment (E) and GE interaction had significant influence (p≤0.01) on sorghum grain yield. Based on AMMI model, BTX TSC-20 followed by ICSB-1808 showed both high yielding and stability across the test environments. However, ICSB-8001 (G11) and BTX-407 (G21), showed maximum stability, but with moderate grain yield. Based on GGE-biplot method, BTX TSC-20 (G25) was the winning genotype for the mega-environment which consists of E1 and E3, ICSB-14 (G3) for the mega-environment (E2 and E4), while BTX 2-1 (G20) for E5 mega-environment, ICSB-88003 (G12) and ICSB-70 (G6) for the mega-environment E6. These genotypes are the most adapted to the respective environments.

Background: Six sigma is a process of quality measurement and improvement program used in industries. Sigma metrics can be used effectively in laboratory services as total testing process has multiple steps and error can occur anywhere. The present study was undertaken to evaluate the quality of the analytical performance of clinical chemistry laboratory by calculating sigma metrics.

Methods: The study was conducted in the clinical biochemistry laboratory of Karwar Institute of Medical Sciences, Karwar. Sigma metrics of 15 parameters with automated chemistry analyzer, transasia XL 640, electrolytes with Roche electrolyte analyzer and thyroid hormones with Maglumi were analyzed.

Results: Sigma values <3 for Urea, ALT, BD, BT, Ca, creatinine (L1) and urea, AST, BD (L2), sodium, potassium and T4 were observed. Sigma lies between 3-6 for Glucose, AST, cholesterol, uric acid, total protein (L1) and ALT, cholesterol, BT, calcium, creatinine and glucose (L2), chloride, T3,TSH.Sigma was more than 6 for Triglyceride, ALP, HDL, albumin (L1) and TG, uric acid, ALP, HDL, albumin, total protein (L2).

Conclusion: Sigma metrics helps to assess analytical methodologies and augment laboratory performance. It acts as a guide for planning quality control strategy. It can be a self assessment tool regarding the functioning of clinical laboratory.

Commercialization of biotechnology can be defined as the conversion of new scientific findings, innovations and discoveries in biotechnology through successful companies and firms or the process by which a product or service in biotechnology is introduced into the general market. Many processes such as sales, production, distribution, marketing, and customer support are required to achieve commercial success. This article deals with factors affecting commercialization of biotechnology in competitive countries and position of biotechnology commercialization in many African countries, including Egypt.