Jun Sun – författare

The Microbiome in Health and Disease, Volume 171 in the Progress in Molecular Biology and Translational Science series, provides the most topical, informative and exciting monographs available on a wide variety of research topics. The series includes in-depth knowledge on the molecular biological aspects of organismal physiology, with this release including chapters on Microbiome in health and disease, CNS development and microbiome in infants, A gut feeling in ALS, Microbiome (Virome) and virus infection, Bugs and Drugs: microbiome in medicine metabolism, Immunity, T cells, and microbiome, Salmonella (Bacterial) infection and cancer: of mice and men, and many other highly researched topics.

Microbiome and the Eye: What's the connection? highlights how alterations in the gut and eye microbiomes can lead to systemic immune alterations with subsequent effects on the eye. The book is divided into two sections, one highlighting how alterations in the gut microbiome impact various components of health outside the gut, with a focus on the immune system and inflammatory mediators, and the second focusing on studies on a variety of ocular diseases, including ocular surface diseases/dry eye, keratitis, uveitis, glaucoma, and retinopathy to gut dysbiosis. With its translational approach, the book is suitable for both researchers and clinicians.

The book will help readers understand the mechanisms in which gut and eye microbiome composition may influence health in multiple compartments, with a focus on eye diseases.

This unique book officially defines microbiome statistics as a specific new field of statistics and addresses the statistical analysis of correlation, association, interaction, and composition in microbiome research. It also defines the study of the microbiome as a hypothesis-driven experimental science and describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigating challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts.

This unique book officially defines microbiome statistics as a specific new field of statistics and addresses the statistical analysis of correlation, association, interaction, and composition in microbiome research. It also defines the study of the microbiome as a hypothesis-driven experimental science and describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigating challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts.

Although the particle swarm optimisation (PSO) algorithm requires relatively few parameters and is computationally simple and easy to implement, it is not a globally convergent algorithm. In Particle Swarm Optimisation: Classical and Quantum Perspectives, the authors introduce their concept of quantum-behaved particles inspired by quantum mechanics, which leads to the quantum-behaved particle swarm optimisation (QPSO) algorithm. This globally convergent algorithm has fewer parameters, a faster convergence rate, and stronger searchability for complex problems.

The book presents the concepts of optimisation problems as well as random search methods for optimisation before discussing the principles of the PSO algorithm. Examples illustrate how the PSO algorithm solves optimisation problems. The authors also analyse the reasons behind the shortcomings of the PSO algorithm.

Moving on to the QPSO algorithm, the authors give a thorough overview of the literature on QPSO, describe the fundamental model for the QPSO algorithm, and explore applications of the algorithm to solve typical optimisation problems. They also discuss some advanced theoretical topics, including the behaviour of individual particles, global convergence, computational complexity, convergence rate, and parameter selection. The text closes with coverage of several real-world applications, including inverse problems, optimal design of digital filters, economic dispatch problems, biological multiple sequence alignment, and image processing. MATLAB®, Fortran, and C++ source codes for the main algorithms are provided on an accompanying downloadable resources.

Helping you numerically solve optimisation problems, this book focuses on the fundamental principles and applications of PSO and QPSO algorithms. It not only explains how to use the algorithms, but also covers advanced topics that establish the groundwork for understanding.

Although the particle swarm optimisation (PSO) algorithm requires relatively few parameters and is computationally simple and easy to implement, it is not a globally convergent algorithm. In Particle Swarm Optimisation: Classical and Quantum Perspectives, the authors introduce their concept of quantum-behaved particles inspired by quantum mechanics, which leads to the quantum-behaved particle swarm optimisation (QPSO) algorithm. This globally convergent algorithm has fewer parameters, a faster convergence rate, and stronger searchability for complex problems.

The book presents the concepts of optimisation problems as well as random search methods for optimisation before discussing the principles of the PSO algorithm. Examples illustrate how the PSO algorithm solves optimisation problems. The authors also analyse the reasons behind the shortcomings of the PSO algorithm.

Moving on to the QPSO algorithm, the authors give a thorough overview of the literature on QPSO, describe the fundamental model for the QPSO algorithm, and explore applications of the algorithm to solve typical optimisation problems. They also discuss some advanced theoretical topics, including the behaviour of individual particles, global convergence, computational complexity, convergence rate, and parameter selection. The text closes with coverage of several real-world applications, including inverse problems, optimal design of digital filters, economic dispatch problems, biological multiple sequence alignment, and image processing. MATLAB®, Fortran, and C++ source codes for the main algorithms are provided on an accompanying downloadable resources.

Helping you numerically solve optimisation problems, this book focuses on the fundamental principles and applications of PSO and QPSO algorithms. It not only explains how to use the algorithms, but also covers advanced topics that establish the groundwork for understanding.

Only recently have we begun to appreciate the role of microbiome in health and disease. Environmental factors and change of life style including diet significantly shape human microbiome that in turn appears to modify gut barrier function affecting nutrient & electrolyte absorption and inflammation. Approaches that can reverse the gut dysbiosis represent as reasonable and novel strategies for restoring the balance between host and microbes.

In the book, we offer summary and discussion on the advances in understanding of pathophysiological mechanisms of microbial host interactions in human diseases. We will not only discuss intestinal bacterial community, but also viruses, fungi and oral microbiome. Microbiome studies will facilitate diagnosis, functional studies, drug development and personalized medicine. Thus, this book will further highlight the microbiome in the context of health and disease, focusing on mechanistic concepts that underlie the complex relationships between host and microbes.

This book offers a summary and discussion of the advances of inflammation and infection in various cancers. The authors cover the classically known virus infections in cancer, novel roles of other pathogens (e.g. bacteria and fungi), as well as biomarkers for diagnosis and therapy. Further, the chapters highlight the progress of immune therapy, stem cells and the role of the microbiome in the pathophysiology of cancers.

This unique book addresses the bioinformatic and statistical modelling and also the analysis of microbiome data using cutting-edge QIIME 2 and R software. It covers core analysis topics in both bioinformatics and statistics, which provides a complete workflow for  microbiome data analysis: from raw sequencing reads to community analysis and statistical hypothesis testing.  It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of QIIME 2 and R for data analysis step-by-step. The data as well as QIIME 2 and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter so that these new methods can be readily applied in their own research.

Bioinformatic and Statistical Analysis of Microbiome Data is an ideal book for advanced graduate students and researchers in the clinical, biomedical, agricultural, and environmental fields, as well as those studying bioinformatics, statistics, and big data analysis.