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Systems and Synthetic Immunology
Systems and Synthetic Immunology focuses on the similarities between biology and engineering at the systems level, which are important for applying engineering theories to biology problems. With the advent of new genomic techniques, there are numerous systematic investigations underway in the scientific world. This volume highlights techniques that can be used to effectively combine two of the most essential biological fields - Systems Biology and Synthetic Immunology. The respective chapters discuss the role of synthetic immunology in biotechnology, production of biomaterials, and their use in vaccine delivery. Further topics include the importance of cytokines; the use of genomic engineering tools in immunotherapy; immunosensors; nanotherapeutics; and bioinformatics tools in biomedical applications. Given its scope, the book offers readers an up-to-date and comprehensive review of this unique and dynamic field of research.
Simulated Evolution and Learning
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Acidophiles
Acidophiles are an important category of microorganisms defined by their ability to withstand and even grow in acidic environments. They are present in terrestrial and marine environments as well as the human body. The diversity, adaptation, and functions of these microorganisms can contribute to the development and application of new biotechnologies for resolving problems of resource exploitation, pollution, and human disease. This book presents breakthroughs and insights into the research on acidophiles. Chapters cover such topics as the two-component system (TCS) in the regulation of the sulfur metabolic process, adaptation mechanisms of acidophiles to low pH, regulation mechanisms and application strategy of quorum sensing in bioleaching bacteria, and Lactobacillus acidophilus and its potential role as a therapeutic for human bone disorders.
Nanomedicine
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Machine and Deep Learning Algorithms and Applications
This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets a...
Integrated Omics Approaches to Infectious Diseases
This book examines applications of multi-omics approaches for understanding disease etiology, pathogenesis, host-pathogen interactions. It also analyzes the genetics, immunological and metabolic mechanisms underlying the infections. The book also explores genomics, transcriptomics, translational-omics, and metabolomics approaches to understand the pathogenesis and identify potential drug targets. It reviews the role of epigenetic reprogramming in shaping the host-pathogen interactions and presents bioinformatics application in the identification of drug targets. Further, it examines the potential applications of RNA sequencing and non-coding RNA profiling to identify the pathogenesis. Lastly, it offers the current challenges, technological advances, and prospects of using multi-omics technologies in infectious biology.
Recurrent Neural Networks
The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the b...
