This chapter intends to consider Pseudomonas versatility as regards the beneficial uses of Pseudomonas for production of primary metabolites including enzymes. This chapter will consider the use of Pseudomonas for production of secondary metabolites from various pathways which are equally useful in the industries and in medicine. Pseudomonas pigments and its usefulness in bioelectricity, medicine, etc. will equally be considered in this chapter. The authors are to integrate knowledge in versatility of Pseudomonas for use as agents of microbial production of biosurfactants for environmental cleanup, restoration, and remediation.

A joint collaboration between the Arctic Centre of the University of Lapland, Finland and the Federal Institute of Industrial Research, Oshodi, Lagos, Nigeria was organised as a hybrid conference on several topics that are related to climate, food, health and entrepreneurship. The utilisation of natural resources in both regions is an important theme in meeting the sustainable development goals agenda. The topics discussed were multidisciplinary, they include Nigerian indigenous foods, bioeconomy, circular economy, nutrition, health, innovation and entrepreneurship under four themes (Climate, Food, Health and Entrepreneurship). There were dignitaries from Finland and Nigeria. The presenters are researchers from Nigerian universities (University of Ibadan, University of Abuja and Eko university, Lagos), Nigerian Federal Institute of Industrial research centre and from the Finnish side we have the university of Lapland, Rovaniemi, University of Oulu, Oulu and the Centria University of Applied Sciences, Kokkola. The topics discussed will serve as training materials for students and learners, the discussion focussed on research opportunities for institutions in both countries. The experts from both countries will continue to dialogue on the possibility of promoting common topics as research agenda in these important areas with the possibilities of creating more jobs.

Artificial intelligence (AI) is the creation of intelligent systems that execute tasks requiring human intelligence to function. The application of AI in bioscience including clinical, agricultural, and pharmaceutical research has revolutionized over the years amplifying productivity and improving delivery time. This chapter explores AI-driven advancements in biological sciences, including genomics, machine learning-based antimicrobial resistance prediction, personalized medicine, AI-assisted drug discovery, precision agriculture, and plant disease detection, examining AI’s transformative potential, addressing challenges, and outlining further directions. By harnessing AI, researchers can accelerate discoveries, improve treatment outcomes, and enhance agricultural productivity, ultimately mitigating the impact of Pseudomonas-related diseases, while promoting healthier environments and advancing clinical, pharmaceutical, and agricultural research. Systems powered by AI-trained models using machine learning (ML) and deep learning (DL) such as deep colony are currently being used in clinics. A total of 5000 culture plates were processed by a large US laboratory involved in the diagnosis of urinary tract infections. Deep colony was able to match the experts’ evaluation and reports in 95.4% instances. Other AI tools, including PotentialNet used in aiding drug design, achieved a significantly high-performance for protein-ligand binding affinity. In the agricultural sector, several DL networks are used to process tabular, non-visual, and visual data such as convolutional neural networks (CNNs), autoencoders and long short-term memory (LSTMs), K-nearest neighbor (K-NN), support vector machine (SVM), K-means classifier, and artificial neural network (ANN) have been employed in identifying plant diseases.