Microbial Products: Applications and Translational Trends offers complete coverage of the production of microbial products, including biopolymers, biofuels, bioactive compounds, and their applications in fields such as bioremediation, agriculture, medicine, and other industrial settings. This book focuses on multiple processes including upstream procedures and downstream processing, and the tools required for their production. Lab-scale development processes may not be as efficient when aiming for large-scale industrial production, so it is necessary to utilize in silico modeling tools for bioprocess design to ensure success at translational levels. Therefore, this book presents in silico and mathematical simulations and approaches used for such applications. Further, it examines microbial products produced from bacteria, fungi, and algae. These major microbial categories have the capacity to produce various, diverse secondary metabolites, bioactive compounds, enzymes, biopolymers, biofuels, probiotics, and more. The bioproducts examined in the book are of great social, medical, and agricultural benefit, and include examples of biodegradable polymers, biofuels, biofertilizers, and drug delivery agents. Presents approaches and tools that aid in the design of eco-friendly, efficient, and economic bioprocesses. Utilizes in silico and mathematical simulations for optimal bioprocess design. Examines approaches to be used for bioproducts from the lab scale to widely applied microbial biotechnologies. Presents the latest trends and technologies in the production approaches for microbial bio-products manufacture and application. This book is ideal for both researchers and academics, as it provides up-to-date knowledge of applied microbial biotechnology approaches for bio-products.

This report presents an integrated outlier detection method, which is named "An Approach to Detect Outlier by Integrating Univariate Outlier Detection and K-means Algorithm." It provides efficient outlier detection and data clustering capabilities in the presence of outliers, and based on filtering of the data after univariate analysis. This algorithm is divided into two stages. The first stage provides Univariate outlier analysis. The main objective of the second stage is an iterative removal of objects, which are far away from their cluster centroids by applying K-means algorithm. The removal occurs according to the minimisation of the value of sum of the distances of all the points to their respective centroid in all the clusters. Finally, we provide experimental results from the application of our algorithm on several datasets to show its effectiveness and usefulness. The empirical results indicate that the proposed method was successful in detecting outliers and promising in practice.