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Biological signal processing can help us gain knowledge about biological complexity, as well as using this knowledge to engineer better systems. Three areas are identified as critical to understanding biology: 1) understanding DNA, 2) examining the overall biological function and 3) evaluating these systems in environmental (ie: turbulent) conditions. DNA is investigated for coding structure and redundancy, and a new tandem repeat region, an indicator of a neurodegenerative disease, is discovered. The linear algebraic framework can be used for further analysis and techniques. The work illustrates how signal processing is a tool to reverse engineer biological systems, and how our better understanding of biology can improve engineering designs. Then, the way a single-cell mobilizes in response to a chemical gradient, known as chemotaxis, is examined. Inspiration from receptor clustering in chemotaxis combined with a Hebbian learning method is shown to improve a gradient-source (chemical/thermal) localization algorithm. The algorithm is implemented, and its performance is evaluated in diffusive and turbulent environments. We then show that sensor cross-correlation can be used in solving chemical localization in difficult turbulent scenarios. This leads into future techniques which can be designed for gradient source tracking. These techniques pave the way for use of biologically-inspired sensor networks in chemical localization.
The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.
Recent Developments in Applied Microbiology and Biochemistry, Vol. 2, provides a comprehensive treatment and understanding on application oriented microbial concepts, giving readers insights into recent developments in microbial biotechnology and medical, agricultural and environmental microbiology. Discusses microbial proteome analyses and their importance in medical microbiology Explores emerging trends in the prevention of current global health problems, such as cancer, obesity and immunity Shows recent approaches in the production of novel enzymes from environmental samples by enrichment culture and metagenomics approaches Guides readers through the status and recent developments in analytical methods for the detection of foodborne microorganisms
This two-volume book presents the outcomes of the 8th International Conference on Soft Computing for Problem Solving, SocProS 2018. This conference was a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), and Vellore Institute of Technology (India), and brought together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions. The book highlights the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers on algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It offers a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems that are difficult to solve using traditional methods.
This is intended to be a simple and accessible book on machine learning methods and their application in computational genomics and nanopore transduction detection. This book has arisen from eight years of teaching one-semester courses on various machine-learning, cheminformatics, and bioinformatics topics. The book begins with a description of ad hoc signal acquisition methods and how to orient on signal processing problems with the standard tools from information theory and signal analysis. A general stochastic sequential analysis (SSA) signal processing architecture is then described that implements Hidden Markov Model (HMM) methods. Methods are then shown for classification and clustering using generalized Support Vector Machines, for use with the SSA Protocol, or independent of that approach. Optimization metaheuristics are used for tuning over algorithmic parameters throughout. Hardware implementations and short code examples of the various methods are also described.
Despite the vital importance of the emerging area of biotechnology and its role in defense planning and policymaking, no definitive book has been written on the topic for the defense policymaker, the military student, and the private-sector bioscientist interested in the "emerging opportunities market" of national security. This edited volume is intended to help close this gap and provide the necessary backdrop for thinking strategically about biology in defense planning and policymaking. This volume is about applications of the biological sciences, here called "biologically inspired innovations," to the military. Rather than treating biology as a series of threats to be dealt with, such innovations generally approach the biological sciences as a set of opportunities for the military to gain strategic advantage over adversaries. These opportunities range from looking at everything from genes to brains, from enhancing human performance to creating renewable energy, from sensing the environment around us to harnessing its power.
The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.
Biological processes in any living organism are based on selective interactions be tween particular biomolecules. In most cases, these interactions involve and are driven by proteins, which are the main conductors of any life process within the organism. The physical nature of these interactions is still not well known. This book presents an entirely new approach to analysis of biomolecular in teractions, in particular protein-protein and protein-DNA interactions, based on the assumption that these interactions are electromagnetic in nature. This new ap proach is the basis of the Resonant Recognition Model (RRM), which was devel oped over the last 15 years. Certain periodicities within the distribution of energies of delocalised electrons along a protein molecule are crucial to the protein's biological function, i.e. inter action with its target. If protein conductivity were introduced, then charges mov ing through the protein backbone might produce electromagnetic irradiation or ab sorption with spectral characteristics corresponding to energy distribution along the protein. The RRM is capable of calculating these spectral characteristics, which we hypothesized would be in the range of the infrared and visible light. These characteristics were confirmed with frequency characteristics obtained ex perimentally for certain light-induced biological processes.