This book explores the impact of nonlinearity on a broad range of areas, including time-honored fields such as biology, geometry, and topology, but also modern ones such as quantum mechanics, networks, metamaterials and artificial intelligence. The concept of nonlinearity is a universal feature in mathematics, physics, chemistry and biology, and is used to characterize systems whose behavior does not amount to a superposition of simple building blocks, but rather features complex and often chaotic patterns and phenomena. Each chapter of the book features a synopsis that not only recaps the recent progress in each field but also charts the challenges that lie ahead. This interdisciplinary book presents contributions from a diverse group of experts from various fields to provide an overview of each field's past, present and future. It will appeal to both beginners and seasoned researchers in nonlinear science, numerous areas of physics (optics, quantum physics, biophysics), and applied mathematics (ODEs, PDEs, dynamical systems, machine learning) as well as engineering.

This book provides a practical guide to the analysis of data from randomized controlled trials (RCT). It gives an answer to the question of how to estimate the intervention effect in an appropriate way. This problem is examined for different RCT designs, such as RCTs with one follow-up measurement, RCTs with more than one follow-up measurement, cluster RCTs, cross-over trials, stepped wedge trials, and N-of-1 trials. The statistical methods are explained in a non-mathematical way and are illustrated by extensive examples. All datasets used in the book are available for download, so readers can reanalyse the examples to gain a better understanding of the methods used. Although most examples are taken from epidemiological and clinical studies, this book is also highly recommended for researchers working in other fields.

This book offers an overview of the statistical methods used in clinical and observational vaccine studies. Pursuing a practical rather than theoretical approach, it presents a range of real-world examples with SAS codes, making the application of the methods straightforward. This revised edition has been significantly expanded to reflect the current interest in this area. It opens with two introductory chapters on the immunology of vaccines to provide readers with the necessary background knowledge. It then continues with an in-depth exploration of the analysis of immunogenicity data. Discussed are, amongst others, maximum likelihood estimation for censored antibody titers, ANCOVA for antibody values, analysis of data of equivalence, and non-inferiority immunogenicity studies. Other topics covered include fitting protection curves to data from vaccine efficacy studies, and the analysis of vaccine safety data. In addition, the book features four new chapters on vaccine field studies: an introductory one, one on randomized vaccine efficacy studies, one on observational vaccine effectiveness studies, and one on the meta-analysis of vaccine efficacy studies. The book offers useful insights for statisticians and epidemiologists working in the pharmaceutical industry or at vaccines institutes, as well as graduate students interested in pharmaceutical statistics.

This book offers essential, systematic information on the assessment of the spatial association between two processes from a statistical standpoint. Divided into eight chapters, the book begins with preliminary concepts, mainly concerning spatial statistics. The following seven chapters focus on the methodologies needed to assess the correlation between two or more processes; from theory introduced 35 years ago, to techniques that have only recently been published. Furthermore, each chapter contains a section on R computations to explore how the methodology works with real data. References and a list of exercises are included at the end of each chapter. The assessment of the correlation between two spatial processes has been tackled from several different perspectives in a variety of applications fields. In particular, the problem of testing for the existence of spatial association between two georeferenced variables is relevant for posterior modeling and inference. One evident application in this context is the quantification of the spatial correlation between two images (processes defined on a rectangular grid in a two-dimensional space). From a statistical perspective, this problem can be handled via hypothesis testing, or by using extensions of the correlation coefficient. In an image-processing framework, these extensions can also be used to define similarity indices between images.

This book reviews recent advances in the emerging field of computational network biology with special emphasis on comparative network analysis and network module detection. The chapters in this volume are contributed by leading international researchers in computational network biology and offer in-depth insight on the latest techniques in network alignment, network clustering, and network module detection. Chapters discuss the advantages of the respective techniques and present the current challenges and open problems in the field. Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection will serve as a great resource for graduate students, academics, and researchers who are currently working in areas relevant to computational network biology or wish to learn more about the field. Data scientists whose work involves the analysis of graphs, networks, and other types of data with topological structure or relations can also benefit from the book's insights.

Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions. In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.

This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks.Discriminant analysis is the best approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data (LSD, Fact 3). However, because most linear discriminant function (LDF) cannot discriminate LSD theoretically and error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM (H-SVM) and Revised IP-OLDF (RIP) can find Fact3 easily. LSD has the Matryoshka structure and is easily decomposed into many SMs (Fact 4). Because all SMs are small samples and LSD, statistical methods analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant range. The maximum RatioSVs of six microarrays is over 11.67%. This fact shows that SV separates two classes by window width (11.67%). Such easy discrimination has been unresolved since 1970. The reason is revealed by facts presented here, so this book can be read and enjoyed like a mystery novel. Many studies point out that it is difficult to separate signal and noise in a high-dimensional gene space. However, the definition of the signal is not clear. Convincing evidence is presented that LSD is a signal. Statistical analysis of the genes contained in the SM cannot provide useful information, but it shows that the discriminant score (DS) discriminated by RIP or H-SVM is easily LSD. For example, the Alon microarray has 2,000 genes which can be divided into 66 SMs. If 66 DSs are used as variables, the result is a 66-dimensional data. These signal data can be analyzed to find malignancy indicators by principal component analysis and cluster analysis.

Reviews of Environmental Contamination and Toxicology attempts to provide concise, critical reviews of timely advances, philosophy and significant areas of accomplished or needed endeavor in the total field of xenobiotics, in any segment of the environment, as well as toxicological implications.

This textbook provides an accessible introduction to physics for undergraduate students in the life sciences, including those majoring in all branches of biology, biochemistry, and psychology and students working on pre-professional programs such as pre-medical, pre-dental, and physical therapy. The text is geared for the algebra-based physics course, often named College Physics in the United States. The order of topics studied are such that most of the problems in the text can be solved with the methods of Statics or Dynamics. That is, they require a free body diagram, the application of Newton’s Laws, and any necessary kinematics. Constructing the text with a standardized problem-solving methodology, simplifies this aspect of the course and allows students to focus on the application of physics to the study of biological systems. Along the way, students apply these techniques to find the tension in a tendon, the sedimentation rate of red blood cells in haemoglobin, the torques and forces on a bacterium employing a flagellum to propel itself through a viscous fluid, and the terminal velocity of a protein moving in a Gel Electrophoresis device. This is part one of a two-volume set; volume 2 introduces students to the conserved-quantities and applies these problem-solving techniques to topics in Thermodynamics, Electrical Circuits, Optics, and Atomic and Nuclear Physics always with continued focus on biological applications.

National Science Foundation (NSF) is a unique federal agency because it supports scientific research financially, but does not engage in scientific work itself. Its history is known only in part because the NSF is a vibrant, expanding, and living entity that makes the final telling of its story impossible. Much can be learned from its beginning as well as its component parts. If the founding of the NSF in 1950 was couched in an era of physics, especially atomic physics, certainly by the end of the 20th century and the beginning of the 21st, biology was, and remains, the queen of sciences for the predictable future. This book highlights the elite status of America's biological sciences as they were funded, affected, and, to a very real degree, interactively guided by the NSF. It examines important events in the earlier history of the Foundation because they play strongly upon the development of the various biology directorates. Issues such as education, applied research, medical science, the National Institutes of Health, the beginnings of biotechnology, and other matters are also discussed.

The book reviews photosynthetic water oxidation and proton-coupled electron transfer in photosystem, focusing on the molecular vibrations of amino acid residues and water molecules. Photosynthetic water oxidation performed by plants and cyanobacteria is essential for the sustenance of life on Earth, not only as an electron source for synthesizing sugars from CO2, but also as an O2 source in the atmosphere. Water oxidation takes place at the Mn4CaO5 cluster in photosystem II, where a series of electron transfer reactions coupled with proton transfer occur using light energy. The author addresses the unresolved mechanisms of photosynthetic water oxidation and relevant proton-coupled electron transfer reactions using a combined approach of experimental and computational methods such as Fourier transform infrared difference spectroscopy and quantum chemical calculations. The results show that protonation and hydrogen-bond structures of water molecules and amino acid residues in the protein play important roles in regulation of the electron and proton transfer reactions. These findings and the methodology make a significant contribution to our understanding the molecular mechanism of photosynthetic water oxidation.

Six Chemicals That Changed Agriculture is a scientific look at how the chemicals used in today's food production were developed, evaluated, and came to be in wide-spread use. From fertilizers to pest management, antibiotics to DNA, chemicals have transformed the way our food is grown, protected, and processed. Agriculture is the world's most important environment interaction, the essential human activity, and an increasingly controversial activity because of its use and presumed misuse of chemistry. The major characteristics of US agriculture for at least the last six decades have been rising productivity, declining number of mid-size farms, increasing farm size, an increasing percentage of farm production on fewer, large farms, increasing dependence of chemical technology and more developmental research being done by the agricultural chemical industry rather than by independent land-grant universities. Another equally important feature of modern agriculture is wide-spread suspicion of its technology by the public. The book will recount examples of this suspicion related to specific chemicals and present the essence of the suspicion and its results.

The Enzymes, Volume 47, highlights new advances in the field, with this new volume presenting interesting chapters on The Multipurpose Family of Oxidases, Vanillyl alcohol oxidase, Choline oxidases, Aryl alcohol oxidase, D- and L-amino acid oxidases, Sugar oxidases, Phenolic Compounds hydroxylases, Baeyer-Villiger Monooxygenases, Flavin-dependent halogenases, Flavin-dependent dehalogenases, Styrene Monooxygenases, Bacterial luciferases, Cellobiose Dehydrogenases, Prenylated flavoenzymes, Ene-reductases, Flavoenzymes in Biocatalysis.

This comprehensive Monograph examines all peripheral and central mechanisms of regulating the secretion of milk from the lactating breast. The author reviews the complete female breast development, starting with the early childhood, up to pregnancy and lactation. Structural and ultrastructural data on the alveolar-ductal system, together with details of breast innervation, are used to investigate the physiological process of lactation. Readers will appreciate the special focus on reflexes, both in the child and the mother, that help to regulate maternal milk production and secretion. Following this, the author sheds a light on the composition and dynamics of milk components during the different periods of lactation. The final section of this volume focusses on practical aspects of modern breastfeeding, like the use of breast pumps or galactogogues, to influence the productivity of milk production. These fundamental principles and structural details will be particularly useful for physiologists and clinicians working in gynecology and pediatrics.

Virology is in a sense both one of the most important precursors and one of the most significant beneficiaries of structural and cellular molecular biology. Numerous breakthroughs in our understanding of the molecular interactions of viruses with host cells are ready for translation into medically important applications such as the prevention and treatment of viral infections. This book collects a wide variety of examples of frontline research into molecular aspects of viral infections from virological, immunological, cell- and molecular-biological, structural, and theoretical perspectives.

The book presents nine mini-courses from a summer school, Dynamics of Biological Systems, held at the University of Alberta in 2016, as part of the prestigious seminar series: Seminaire de Mathematiques Superieures (SMS). It includes new and significant contributions in the field of Dynamical Systems and their applications in Biology, Ecology, and Medicine. The chapters of this book cover a wide range of mathematical methods and biological applications. They - explain the process of mathematical modelling of biological systems with many examples, - introduce advanced methods from dynamical systems theory, - present many examples of the use of mathematical modelling to gain biological insight - discuss innovative methods for the analysis of biological processes, - contain extensive lists of references, which allow interested readers to continue the research on their own. Integrating the theory of dynamical systems with biological modelling, the book will appeal to researchers and graduate students in Applied Mathematics and Life Sciences.

The novel coronavirus 2019 (COVID-19) has caused a serious global pandemic in just eight months. Nearly every country and territory in the world has been affected by the virus. The virulence and infection rate of the virus are profound, and has required extreme social distancing measures across the globe in order to prevent overwhelming the healthcare services and hospitals. COVID-19 appears to have the greatest effects on elderly individuals and those who have co-morbid diseases, such as heart disease, asthma, and diabetes. As the peak begins to slow in many countries, the death rates remain high amidst justified fears of a second wave. A rapid worldwide mobilization has begun to identify effective treatments and develop vaccines. This new volume will increase readers' understanding of the ongoing COVID-19 pandemic through a series of chapters that address these concerns. Leading experts will discuss the effects of the virus in cases of co-morbidities, new treatment approaches, mental health aspects of the pandemic, and convey the results of survey studies. The book will be an excellent resource for researchers studying virology, metabolic diseases, respiratory disorders, and clinical scientists, physicians, drug companies, and healthcare services and workers.

This book commemorates the scientific contributions of distinguished statistician, Andrei Yakovlev. It reflects upon Dr. Yakovlev's many research interests including stochastic modeling and the analysis of micro-array data, and throughout the book it emphasizes applications of the theory in biology, medicine and public health. The contributions to this volume are divided into two parts. Part A consists of original research articles, which can be roughly grouped into four thematic areas: (i) branching processes, especially as models for cell kinetics, (ii) multiple testing issues as they arise in the analysis of biologic data, (iii) applications of mathematical models and of new inferential techniques in epidemiology, and (iv) contributions to statistical methodology, with an emphasis on the modeling and analysis of survival time data. Part B consists of methodological research reported as a short communication, ending with some personal reflections on research fields associated with Andrei and on his approach to science. The Appendix contains an abbreviated vitae and a list of Andrei's publications, complete as far as we know. The contributions in this book are written by Dr. Yakovlev's collaborators and notable statisticians including former presidents of the Institute of Mathematical Statistics and of the Statistics Section of the AAAS. Dr. Yakovlev's research appeared in four books and almost 200 scientific papers, in mathematics, statistics, biomathematics and biology journals. Ultimately this book offers a tribute to Dr. Yakovlev's work and recognizes the legacy of his contributions in the biostatistics community.

This book is comprised of presentations delivered at the 5th Workshop on Biostatistics and Bioinformatics held in Atlanta on May 5-7, 2017. Featuring twenty-two selected papers from the workshop, this book showcases the most current advances in the field, presenting new methods, theories, and case applications at the frontiers of biostatistics, bioinformatics, and interdisciplinary areas. Biostatistics and bioinformatics have been playing a key role in statistics and other scientific research fields in recent years. The goal of the 5th Workshop on Biostatistics and Bioinformatics was to stimulate research, foster interaction among researchers in field, and offer opportunities for learning and facilitating research collaborations in the era of big data. The resulting volume offers timely insights for researchers, students, and industry practitioners.

This book reviews all important aspects of dietary research associated with cancer with the aim of shedding new light on these conditions through combined understanding of traditional and new paradigms. The book is divided into 17 chapters, the first portion reinterprets healthy diets for cancer based on up-to-date evidence from a network science perspective, examining the dietary patterns, outcome of diet related clinical trials, emerging framework of molecular mechanisms and interactions of dietary interventions and their applications in personalized diet, ground realities of benefits and regulatory frame work for functional foods, nutraceuticals and supplements in cancer prevention and upcoming future prospectus in diet-cancer research.. The later part of the book discusses recent advances in understanding of the elaborative discourse on cancer and fasting, covering, for example, calorie restriction and fasting mimicking diet. Finally, different Dietary research and approaches are considered in the context of novel intervention for cancer research. Dietary Research in Cancer will be of interest for all researchers, nutritionists, students and clinicians in the field.