Alazar Yeshitila’s research while affiliated with Addis Ababa Science and Technology University and other places

Gesho ( Rhamnus prinoides ) is a medicinal plant with antioxidant and anti-inflammatory activities commonly used in the ethnomedicinal systems of Africa. Using a three-layer neural network, four culture conditions viz., concentration of agar, duration of light exposure, temperature of culture, and relative humidity were used to calculate the callus differentiation rate of gesho. With the ability to quickly identify optimal solutions using high-speed computers, synthetic neural networks have emerged as a rapid, reliable, and accurate fitting technique. They also have the self-directed learning capability that is essential for accurate prediction. The network's final architecture for four selected variables and its performance has been confirmed with high correlation coefficient (R ² , 0.9984) between the predicted and actual outputs and the root-mean-square error of 0.0249, were developed after ten-fold cross validation as the training function. In vitro research had been conducted using the genetic algorithm’s suggestions for the optimal culture conditions. The outcomes demonstrated that the actual gesho differentiation rate was 93.87%, which was just 1.86% lesser than the genetic algorithm's predicted value. The projected induced differentiation rate was 87.62%, the actual value was 84.79%, and the predicted value was 2.83% higher than Response Surface Methods optimisation. The environment for the growth of plant tissue can be accurately and efficiently optimised using a genetic algorithm and an artificial neural network. Further biological investigations will presumably utilise this technology.

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.

The intensification of agricultural sectors tend to have overuse and misuse of agrochemicals such as inorganic fertilizers (for increasing farm productivity) and biocides (for controlling of unwanted groups such as weeds, parasites, insects, pathogen and pests). Thereby, this has been resulted in pollution of water environments (rivers, lakes, aquifers and coastal waters) and land organism. This ultimately brings great impacts and conveys the loss of ecological functioning and threatening of life in the ecosystem. Consequently, there is the need to find effective methods for the detoxification of agrochemical residue from contaminated environments or reduction of agrochemical use via substituting eco-friendly options. Among various approaches, which are important to minimize agrochemical impact on the ecosystem, microbial remediation (especially the use of bacteria) is cost-effective, efficient, ecologically friendly, and important to transform and/or mineralize contaminants into carbon dioxide, water, energy and microbial biomass. The effectiveness of bacterial remediation, however, depends on their catabolic genes, diverse metabolic pathways, presence or absence of specific enzymes, physicochemical and biological factors and stress tolerance. The conventional application of bacteria in the agriculture for the detoxification of agrochemical waste or for the application of alternative biofertilizer and biopesticide was limited due several reasons. Therefore, supporting the conventional one with genetically modified bacteria to minimize the impact of agrochemicals on the ecosystem is the finest alternative. Therefore, via addressing the impact of agrochemicals residue in the ecosystem, this review focused on the mechanism of genetically modified bacteria to minimize the impact of agrochemicals in the ecosystem.KeywordsBioremediationAgrochemicalsBiofertilizerGenetically modified bacteriaBiopesticide