Writing in the Biological Sciences: A Comprehensive Guide to Scientific Communication serves as a comprehensive one-stop reference guide to scientific writing and communication for budding professionals in the life sciences and related fields. The book is designed to function both as a free-standing textbook for a course on writing in the sciences and as an accompanying text or reference guide in courses with writing-intensive components. It covers all the basics of scientific communication that students need to know and master for successful scientific careers. The book lays the foundation for professional writing by starting with basic scientific writing principles and then by applying these principles to lab reports, summaries, and critiques and eventually also to full-fledged scientific research articles, review articles, and grant proposals. Practical advice for organizing academic presentations and posters as well as for putting together job applications is also included.

Exercise and Disease Management is designed to help managed care physicians, their patients, other health care professionals, and interested readers integrate current exercise guidelines into their practices. This extraordinary book is accompanied by a series of 11 workbooks, each one for a chronic disease, designed specifically for physicians to give to their patients. These workbooks make it convenient for physicians to prescribe physical activity to their patients in a ready-to-use format. Each book chapter and workbook contains a section on the background, medical management, and exercise guidelines, accompanied by self-care instructions for patients, encouraging them to take a proactive role in their health and disease management. New and Updated in the Second Edition: A ready-to-use "Exercise Prescription Page," which follows each chapter on a specific disease, enables health care professionals, especially physicians in managed care, to prescribe exercise quickly to clients with specific medical conditions The "Rate Pressure Product" method for prescribing exercise helps health care providers individualize exercise prescriptions for patients with heart disease by accounting for the amount of oxygen the heart uses Individual companion workbooks on the downloadable resources provide patient health maintenance information about diabetes, AIDS, obesity, golden years (age 65 and older), heart, kidney, peripheral arterial, and lung disease. Workbooks for physical inactivity, osteoporosis, arthritis and high blood pressure also are included on the downloadable resources Common question-and-answer sections that help patients understand the diseases from which they suffer and encourages them to take responsibility for their health Exercise and Disease Management, Second Edition consolidates the current knowledge base on exercise and chronic disease, providing a ready-made format for health care providers to use when prescribing exercise programs for their clients. Using guidelines set forth by the American College of Sports Medicine and the American Association of Cardiovascular and Pulmonary Rehabilitation, this book helps physicians, other health care providers, and health enthusiasts respond to the challenge to keep patients healthier and active and reduce recurrent hospitalizations and health care costs.

The aim of this book is to introduce the simulation of various physical fields and their applications for biomedical engineering, which will provide a base for researchers in the biomedical field to conduct further investigation. The entire book is classified into three levels. It starts with the first level, which presents the single physical fields including structural analysis, fluid simulation, thermal analysis, and acoustic modeling. Then, the second level consists of various couplings between two physical fields covering structural thermal coupling, porous media, fluid structural interaction (FSI), and acoustic FSI. The third level focuses on multi-coupling that coupling with more than two physical fields in the model. Each part in all levels is organized as the physical feature, finite element implementation, modeling procedure in ANSYS, and the specific applications for biomedical engineering like the FSI study of Abdominal Aortic Aneurysm (AAA), acoustic wave transmission in the ear, and heat generation of the breast tumor. The book should help for the researchers and graduate students conduct numerical simulation of various biomedical coupling problems. It should also provide all readers with a better understanding of various couplings.

Spectral, Photon Counting Computed Tomography is a comprehensive cover of the latest developments in the most prevalent imaging modality (x-ray computed tomography (CT)) in its latest incarnation: Spectral, Dual-Energy, and Photon Counting CT. Disadvantages of the conventional single-energy technique used by CT technology are that different materials cannot be distinguished and that the noise is larger. To address these problems, a novel spectral CT concept has been proposed. Spectral Dual-Energy CT (DE-CT) acquires two sets of spectral data, and Spectral Photon Counting CT (PC-CT) detects energy of x-ray photons to reveal additional material information of objects by using novel energy-sensitive, photon-counting detectors. The K-edge imaging may be a gateway for functional or molecular CT. The book covers detectors and electronics, image reconstruction methods, image quality assessments, a simulation tool, nanoparticle contrast agents, and clinical applications for spectral CT.

Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

The Third Edition of Biology of Fishes is chiefly about fish as remarkably efficient machines for coping with the many problems that life in water entails, and looks at many such special cases. Fishes form the largest group of vertebrates, with around 20,000 known species, and they display a remarkable diversity of size, shape, internal structure and ecology to cope with environments ranging from transient puddles to the abyssal depths of the sea. Biology of Fishes does not try to cover all aspects of fish biology, but focuses on the ingenious ways in which fish have resolved the particular problems that come from living in water, especially body fluid regulation, locomotion, feeding mechanisms, and sensory systems. Enough detail is provided for the reader to be able to go on and use primary research papers. Each chapter has been thoroughly updated and a new chapter on the immune system has been added. This is an ideal textbook for students of fish biology and any of the branches of aquatic biology. Given its skilful combination of breadth and detail, the book also provides a manageable review of fish biology for experienced biologists.

Biologists, climate scientists, and economists all rely on models to move their work forward. In this book, Stephen M. Downes explores the use of models in these and other fields to introduce readers to the various philosophical issues that arise in scientific modeling. Readers learn that paying attention to models plays a crucial role in appraising scientific work. This book first presents a wide range of models from a number of different scientific disciplines. After assembling some illustrative examples, Downes demonstrates how models shed light on many perennial issues in philosophy of science and in philosophy in general. Reviewing the range of views on how models represent their targets introduces readers to the key issues in debates on representation, not only in science but in the arts as well. Also, standard epistemological questions are cast in new and interesting ways when readers confront the question, "What makes for a good (or bad) model?" All examples from the sciences and positions in the philosophy of science are presented in an accessible manner. The book is suitable for undergraduates with minimal experience in philosophy and an introductory undergraduate experience in science. Key features: The book serves as a highly accessible philosophical introduction to models and modeling in the sciences, presenting all philosophical and scientific issues in a nontechnical manner. Students and other readers learn to practice philosophy of science by starting with clear examples taken directly from the sciences. While not comprehensive, this book introduces the reader to a wide range of views on key issues in the philosophy of science.

Managing Natural Science Collections demonstrates the need for consistency and evidence-based decision making in the management of natural science collections, which are becoming increasingly valuable when it comes to addressing societal challenges. Drawing upon the experience of four experts who have managed some of the largest and most diverse collections in the world, the book aims to assist in the making of strategic and operational decisions regarding care, development, access and resource management. Encouraging the reader to consider how collection strategies can be aligned with the mission of their institution and contribute to its vision, the authors also examine ways to deliver a consistent approach that will secure the present and future availability and relevance of collections. Principles of good practice and resource optimisation in an ethical and legal context are provided throughout the book, as well as case studies, sample documents and templates, all of which will be useful for discussion and teaching. Managing Natural Science Collections encourages each reader to consider the different options available to them. As such, it should be essential reading for museum practitioners and other professionals around the world who are involved with any strategic aspect of managing natural science collections. Students of museum studies will also find much to interest them within the pages of this book.

Modern Directional Statistics collects important advances in methodology and theory for directional statistics over the last two decades. It provides a detailed overview and analysis of recent results that can help both researchers and practitioners. Knowledge of multivariate statistics eases the reading but is not mandatory. The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional supports, and computational and graphical methods. Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein's Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Societe Francaise de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics. Thomas Verdebout is professor of mathematical statistics at Universite libre de Bruxelles (ULB). His main research interests are semi-parametric statistics, high- dimensional statistics, directional statistics and rank-based procedures. He has won an annual prize of the Belgian Academy of Sciences and is an elected member of the International Statistical Institute. He is associate editor for the journals Statistics and Probability Letters and Journal of Multivariate Analysis.

Though the book's main focus is on data analysis, it will also treat other aspects linked to population genomics, including theory (Chap. 1) and sampling and lab data procedures All types of data and organims will be covered Emphasis on open source software Applications in molecular identification in a specific chapte Case studies and exercises throughout the book

John C. Gunn, CBE FRCPsych FMedSci Member, Parole Board, England & Wales, Emeritus Professor of Forensic Psychiatry, Institute of Psychiatry, King's College London, UK Pamela J. Taylor, FRCPsych FMedSci, Professor of Forensic Psychiatry, School of Medicine, Cardiff University, UK

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics. One of the applications in the book is a three-state process for dementia and survival in the older population. This process is described by an illness-death model with a dementia-free state, a dementia state, and a dead state. Statistical modelling of a multi-state process can investigate potential associations between the risk of moving to the next state and variables such as age, gender, or education. A model can also be used to predict the multi-state process. The methods are for longitudinal data subject to interval censoring. Depending on the definition of a state, it is possible that the time of the transition into a state is not observed exactly. However, when longitudinal data are available the transition time may be known to lie in the time interval defined by two successive observations. Such an interval-censored observation scheme can be taken into account in the statistical inference. Multi-state modelling is an elegant combination of statistical inference and the theory of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide range of applications.

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture-recapture data

Learn how to think and engage like a scientist! BIOLOGY: THE DYNAMIC SCIENCE, 2e, International Edition, provides you with a deep understanding of the core concepts in Biology, building a strong foundation for additional study. In a fresh presentation, the authors explain complex ideas clearly and describe how biologists collect and interpret evidence to test hypotheses about the living world. Russell, Hertz, and McMillan will spark your curiosity about living systems instead of burying it under a mountain of disconnected facts. You will learn what scientists know about the living world, how they know it, and what they still need to learn. The accompanying Aplia for Biology interactively guides you through the thought processes and procedures that scientists use in their research and helps you apply and synthesize content from the text. Overall, you will learn how to think like a scientist and engage in the scientific process yourself.

This book reviews the state-of-the-art advances in skew-elliptical distributions and provides many new developments in a single volume, collecting theoretical results and applications previously scattered throughout the literature. The main goal of this research area is to develop flexible parametric classes of distributions beyond the classical normal distribution. The book is divided into two parts. The first part discusses theory and inference for skew-elliptical distribution. The second part examines applications and case studies, including areas such as economics, finance, oceanography, climatology, environmetrics, engineering, image processing, astronomy, and biomedical science.

Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research. Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current status data, Bayesian semiparametric regression analysis of interval-censored data with monotone splines, Bayesian inferential models for interval-censored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for interval-censored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages. A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.

Informatics in Medical Imaging provides a comprehensive survey of the field of medical imaging informatics. In addition to radiology, it also addresses other specialties such as pathology, cardiology, dermatology, and surgery, which have adopted the use of digital images. The book discusses basic imaging informatics protocols, picture archiving and communication systems, and the electronic medical record. It details key instrumentation and data mining technologies used in medical imaging informatics as well as practical operational issues, such as procurement, maintenance, teleradiology, and ethics. Highlights Introduces the basic ideas of imaging informatics, the terms used, and how data are represented and transmitted Emphasizes the fundamental communication paradigms: HL7, DICOM, and IHE Describes information systems that are typically used within imaging departments: orders and result systems, acquisition systems, reporting systems, archives, and information-display systems Outlines the principal components of modern computing, networks, and storage systems Covers the technology and principles of display and acquisition detectors, and rounds out with a discussion of other key computer technologies Discusses procurement and maintenance issues; ethics and its relationship to government initiatives like HIPAA; and constructs beyond radiology The technologies of medical imaging and radiation therapy are so complex and computer-driven that it is difficult for physicians and technologists responsible for their clinical use to know exactly what is happening at the point of care. Medical physicists are best equipped to understand the technologies and their applications, and these individuals are assuming greater responsibilities in the clinical arena to ensure that intended care is delivered in a safe and effective manner. Built on a foundation of classic and cutting-edge research, Informatics in Medical Imaging supports and updates medical physicists functioning at the intersection of radiology and radiation.

In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes: Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research Software and methodological platforms, including open source software packages, RunMyCode platform, and open access journals Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org.

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis.

The book explores Bayesian techniques and models for detecting differentially expressed genes, classifying differential gene expression, and identifying biomarkers. It develops novel Bayesian nonparametric approaches for bioinformatics problems, measurement error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also describes applications of mode-oriented stochastic search algorithms, in vitro to in vivo factor profiling, proportional hazards regression using Bayesian kernel machines, and QTL mapping.

Focusing on design, statistical inference, and data analysis from a Bayesian perspective, this volume explores statistical challenges in bioinformatics data analysis and modeling and offers solutions to these problems. It encourages readers to draw on the evolving technologies and promote statistical development in this area of bioinformatics.

Enzyme-Based Organic Synthesis An insightful exploration of an increasingly popular technique in organic chemistry In Enzyme-Based Organic Synthesis, expert chemist Dr. Cheanyeh Cheng delivers a comprehensive discussion of the principles, methods, and applications of enzymatic and microbial processes for organic synthesis. The book thoroughly explores this growing area of green synthetic organic chemistry, both in the context of academic research and industrial practice. The distinguished author provides a single point of access for enzymatic methods applicable to organic synthesis and focuses on enzyme catalyzed organic synthesis with six different classes of enzyme. This book serves as a link between enzymology and biocatalysis and serves as an invaluable reference for the growing number of organic chemists using biocatalysis. Enzyme-Based Organic Synthesis provides readers with multiple examples of practical applications of the main enzyme classes relevant to the pharmaceutical, medical, food, cosmetics, fragrance, and health care industries. Readers will also find: A thorough introduction to foundational topics, including the discovery and nature of enzymes, enzyme structure, catalytic function, molecular recognition, enzyme specificity, and enzyme classes Practical discussions of organic synthesis with oxidoreductases, including oxidation reactions and reduction reactions Comprehensive explorations of organic synthesis with transferases, including transamination with aminotransferases and phosphorylation with kinases In-depth examinations of organic synthesis with hydrolases, including the hydrolysis of the ester bond Perfect for organic synthetic chemists, chemical and biochemical engineers, biotechnologists, process chemists, and enzymologists, Enzyme-Based Organic Synthesis is also an indispensable resource for practitioners in the pharmaceutical, food, cosmetics, and fragrance industries that regularly apply this type of synthesis.

Too often, healthcare workers are led to believe that medical informatics is a complex field that can only be mastered by teams of professional programmers. This is simply not the case. With just a few dozen simple algorithms, easily implemented with open source programming languages, you can fully utilize the medical information contained in clinical and research datasets. The common computational tasks of medical informatics are accessible to anyone willing to learn the basics. Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python, and Ruby demonstrates that biomedical professionals with fundamental programming knowledge can master any kind of data collection. Providing you with access to data, nomenclatures, and programming scripts and languages that are all free and publicly available, this book - Describes the structure of data sources used, with instructions for downloading Includes a clearly written explanation of each algorithm Offers equivalent scripts in Perl, Python, and Ruby, for each algorithm Shows how to write short, quickly learned scripts, using a minimal selection of commands Teaches basic informatics methods for retrieving, organizing, merging, and analyzing data sources Provides case studies that detail the kinds of questions that biomedical scientists can ask and answer with public data and an open source programming language Requiring no more than a working knowledge of Perl, Python, or Ruby, Methods in Medical Informatics will have you writing powerful programs in just a few minutes. Within its chapters, you will find descriptions of the basic methods and implementations needed to complete many of the projects you will encounter in your biomedical career.

and for those interested in toxic effects of chemicals on humans, Human Variability in Response to Chemical Exposures: Measures, Modeling, and Risk Assessment recognizes and addresses the increasing awareness that individual biological differences be reflected when assessing human health risks associated with exposure to chemicals. Eight original manuscripts, commissioned by the ILSI Risk Science Institute, address the evidence for variability in human response to chemicals associated with reproductive and developmental effects, effects on the nervous system and lungs, and cancer. Their reports convey both the current state of scientific understanding of response variability and the genetic basis for such observations. This book recognizes that understanding of variability in response is critical in accounting for interindividual variability in susceptibility and, hence, risk, if the regulatory community and others are expected to characterize human health risks associated with exposure to chemicals. Models for incorporating measures of response variability in the risk assessment process are critically reviewed and illustrated with published data. This authoritative work indicates that, in the case of certain chemicals and in the context of certain specific toxic effects, we have considerable ability to predictively and quantitatively characterize human variability, but, in the majority of cases, our ability to do so is limited. If we improve both quantity and quality of information available on response variability and increase our understanding of target tissue dosimetry, we should be better able to account for variability in human susceptibility to the toxic effects of chemicals.

Take Your NI Trial to the Next Level Reflecting the vast research on noninferiority (NI) designs from the past 15 years, Noninferiority Testing in Clinical Trials: Issues and Challenges explains how to choose the NI margin as a small fraction of the therapeutic effect of the active control in a clinical trial. Requiring no prior knowledge of NI testing, the book is easily accessible to both statisticians and nonstatisticians involved in drug development. With over 20 years of experience in this area, the author introduces the basic elements of the NI trials one at a time in a logical order. He discusses issues with estimating the effect size based on historical placebo control trials of the active control. The book covers fundamental concepts related to NI trials, such as assay sensitivity, constancy assumption, discounting, and preservation. It also describes patient populations, three-arm trials, and the equivalence of three or more groups.

Develop a Deep Understanding of the Statistical Issues of APC Analysis Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. See How Common Methods Are Related to Each Other After a general and wide-ranging introductory chapter, the book explains the identification problem from algebraic and geometric perspectives and discusses constrained regression. It then covers important strategies that provide information that does not directly depend on the constraints used to identify the APC model. The final chapter presents a specific empirical example showing that a combination of the approaches can make a compelling case for particular APC effects. Get Answers to Questions about the Relationships of Ages, Periods, and Cohorts to Important Substantive Variables This book incorporates several APC approaches into one resource, emphasizing both their geometry and algebra. This integrated presentation helps researchers effectively judge the strengths and weaknesses of the methods, which should lead to better future research and better interpretation of existing research.