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.

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.

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.

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.

Biopolitics at 50 Years: Founding and Evolution explores the study of biology and politics through the prism of fifty years of experience presenting current research that illustrates the nature and evolution of biopolitics. Containing substantive chapters that address many issues using different methodologies, Biopolitics at 50 Years draws on different theoretical perspectives to advance the field. Beginning with a reflection on the origin and scholarly emphasises of biopolitics and concludes with future prospects in the field, this 13th volume of Research in Biopolitics explores the broad scale theoretical consideration of politics based on evolutionary factors affecting the political realm physiological factors affecting political behavior, public policy issues affected by biology and how human nature affects outcomes of policy making.

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.

Since 1945, "The Annual Deming Conference on Applied Statistics" has been an important event in the statistics profession. In Clinical Trial Biostatistics and Biopharmaceutical Applications, prominent speakers from past Deming conferences present novel biostatistical methodologies in clinical trials as well as up-to-date biostatistical applications from the pharmaceutical industry. Divided into five sections, the book begins with emerging issues in clinical trial design and analysis, including the roles of modeling and simulation, the pros and cons of randomization procedures, the design of Phase II dose-ranging trials, thorough QT/QTc clinical trials, and assay sensitivity and the constancy assumption in noninferiority trials. The second section examines adaptive designs in drug development, discusses the consequences of group-sequential and adaptive designs, and illustrates group sequential design in R. The third section focuses on oncology clinical trials, covering competing risks, escalation with overdose control (EWOC) dose finding, and interval-censored time-to-event data. In the fourth section, the book describes multiple test problems with applications to adaptive designs, graphical approaches to multiple testing, the estimation of simultaneous confidence intervals for multiple comparisons, and weighted parametric multiple testing methods. The final section discusses the statistical analysis of biomarkers from omics technologies, biomarker strategies applicable to clinical development, and the statistical evaluation of surrogate endpoints. This book clarifies important issues when designing and analyzing clinical trials, including several misunderstood and unresolved challenges. It will help readers choose the right method for their biostatistical application. Each chapter is self-contained with references.

Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http: //CRAN.R-project.org

After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes' test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomp package in R, which offers a convenient interface to perform multiple comparisons in a general context. Following this theoretical framework, the book explores applications involving the Dunnett test, Tukey's all pairwise comparisons, and general multiple contrast tests for standard regression models, mixed-effects models, and parametric survival models. The last chapter reviews other multiple comparison procedures, such as resampling-based procedures, methods for group sequential or adaptive designs, and the combination of multiple comparison procedures with modeling techniques.

Controlling multiplicity in experiments ensures better decision making and safeguards against false claims. A self-contained introduction to multiple comparison procedures, this book offers strategies for constructing the procedures and illustrates the framework for multiple hypotheses testing in general parametric models. It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa.

See Dr. Bretz discuss the book.

The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout. Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.

New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive biological understanding. A complilation of recent approaches from prominent researchers, Bioinformatics: High Performance Parallel Computer Architectures discusses how to take advantage of bioinformatics applications and algorithms on a variety of modern parallel architectures. Two factors continue to drive the increasing use of modern parallel computer architectures to address problems in computational biology and bioinformatics: high-throughput techniques for DNA sequencing and gene expression analysis-which have led to an exponential growth in the amount of digital biological data-and the multi- and many-core revolution within computer architecture. Presenting key information about how to make optimal use of parallel architectures, this book: Describes algorithms and tools including pairwise sequence alignment, multiple sequence alignment, BLAST, motif finding, pattern matching, sequence assembly, hidden Markov models, proteomics, and evolutionary tree reconstruction Addresses GPGPU technology and the associated massively threaded CUDA programming model Reviews FPGA architecture and programming Presents several parallel algorithms for computing alignments on the Cell/BE architecture, including linear-space pairwise alignment, syntenic alignment, and spliced alignment Assesses underlying concepts and advances in orchestrating the phylogenetic likelihood function on parallel computer architectures (ranging from FPGAs upto the IBM BlueGene/L supercomputer) Covers several effective techniques to fully exploit the computing capability of many-core CUDA-enabled GPUs to accelerate protein sequence database searching, multiple sequence alignment, and motif finding Explains a parallel CUDA-based method for correcting sequencing base-pair errors in HTSR data Because the amount of publicly available sequence data is growing faster than single processor core performance speed, modern bioinformatics tools need to take advantage of parallel computer architectures. Now that the era of the many-core processor has begun, it is expected that future mainstream processors will be parallel systems. Beneficial to anyone actively involved in research and applications, this book helps you to get the most out of these tools and create optimal HPC solutions for bioinformatics.

Statistical and mathematical models are defined by parameters that describe different characteristics of those models. Ideally it would be possible to find parameter estimates for every parameter in that model, but, in some cases, this is not possible. For example, two parameters that only ever appear in the model as a product could not be estimated individually; only the product can be estimated. Such a model is said to be parameter redundant, or the parameters are described as non-identifiable. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context. Key features of this book: Detailed discussion of the problems caused by parameter redundancy and non-identifiability Explanation of the different general methods for detecting parameter redundancy and non-identifiability, including symbolic algebra and numerical methods Chapter on Bayesian identifiability Throughout illustrative examples are used to clearly demonstrate each problem and method. Maple and R code are available for these examples More in-depth focus on the areas of discrete and continuous state-space models and ecological statistics, including methods that have been specifically developed for each of these areas This book is designed to make parameter redundancy and non-identifiability accessible and understandable to a wide audience from masters and PhD students to researchers, from mathematicians and statisticians to practitioners using mathematical or statistical models.

State-of-the-Art Methods for Drug Safety Assessment Responding to the increased scrutiny of drug safety in recent years, Quantitative Evaluation of Safety in Drug Development: Design, Analysis and Reporting explains design, monitoring, analysis, and reporting issues for both clinical trials and observational studies in biopharmaceutical product development. It presents the latest statistical methods for drug safety assessment. The book's three sections focus on study design, safety monitoring, and data evaluation/analysis. The book addresses key challenges across regulatory agencies, industry, and academia. It discusses quantitative approaches to safety evaluation and risk management in drug development, covering Bayesian methods, effective safety graphics, and risk-benefit evaluation. Written by a team of experienced leaders, this book brings the most advanced knowledge and statistical methods of drug safety to the statistical, clinical, and safety community. It shares best practices and stimulates further research and methodology development in the drug safety area.

With more and more interest in how components of biological systems interact, it is important to understand the various aspects of systems biology. Kinetic Modelling in Systems Biology focuses on one of the main pillars in the future development of systems biology. It explores both the methods and applications of kinetic modeling in this emerging field. The book introduces the basic biological cellular network concepts in the context of cellular functioning, explains the main aspects of the Edinburgh Pathway Editor (EPE) software package, and discusses the process of constructing and verifying kinetic models. It presents the features, user interface, and examples of DBSolve as well as the principles of modeling individual enzymes and transporters. The authors describe how to construct kinetic models of intracellular systems on the basis of models of individual enzymes. They also illustrate how to apply the principles of kinetic modeling to collect all available information on the energy metabolism of whole organelles, construct a kinetic model, and predict the response of the organelle to changes in external conditions. The final chapter focuses on applications of kinetic modeling in biotechnology and biomedicine. Encouraging readers to think about future challenges, this book will help them understand the kinetic modeling approach and how to apply it to solve real-life problems. Downloadable Resources FeaturesExtensively used throughout the text for pathway visualization and illustration, the EPE software is available on the accompanying downloadable resources. The downloadable resources also include pathway diagrams in several graphical formats, DBSolve installation with examples, and all models from the book with dynamic visualization of simulation results, allowing readers to perform in silico simulations and use the models as templates for further applications.

In the 1940s, the physician and natural scientist Dr. Wilhelm Reich claimed discovery of a new form of energy which charged up living organisms and also existed in the open atmosphere and in high vacuum. Reich's laboratory and clinical findings indicated this new energy, which he called the orgone, could be photographed and measured, and had powerful life-positive biological effects. Reich trained other scientists and physicians in his findings, and together they set about applying the inexpensive orgone treatment methods - using a device called the orgone energy accumulator - against various illness, including cancer, with remarkably good results. His published findings shocked the scientific world of his day, however, ultimately leading to numerous smear articles in the popular press, and trumped-up charges by a power-drunk Food and Drug Administration. The FDA "investigation" lead to a court trial of much greater significance than the better-known "Scopes Monkey Trial." Ignoring Reich's evidence and declaring "the orgone energy does not exist," US Courts ordered all his books on the orgone subject to be burned, and banned from further circulation. Reich was also thrown into prison, where he died. His work was nearly forgotten except by a small group of supporters. In this Handbook, former university professor Dr. James DeMeo examines Reich's evidence and reports on his own observations and laboratory experiments, which have repeatedly confirmed the reality of the orgone phenomenon. DeMeo also surveys the observations and experiments of others, including controlled cancer mice experiments, double-blind university studies, and clinical reports from physicians working in private clinics where use of Reich's controversial orgone energy accumulator proceeds today. This Handbook also gives a warning about low-level atomic and electromagnetic radiations, as from nuclear power plants, power-line fields and cell-phones, along with advice on measurement and protection against such toxic energy. Also discussed is the subject of healing waters, or Living Waters from natural hot springs, a form of energy medicine which once was widely used in North America before the rise of the authoritarian MD-hospital system and the powerful federal bureaucracy of the FDA. Dr. DeMeo also gives detailed construction plans for people to build their own orgone energy blankets and accumulators, which are inexpensive and simple to construct, though requiring specific direction as to their materials and environments. This is the Third Revised and Expanded 2010 Edition of the Orgone Accumulator Handbook, nearly 100 pages larger than prior editions and carrying a Foreword by Dr. Eva Reich (the daughter of Dr. Wilhelm Reich), along with many photos, diagrams and charts. It is updated to address new issues about the best materials for orgone accumulator and blanket construction. An Appendix is also included, identifying the similarities of Reich's orgone energy to the cosmic ether and "dark matter" of modern physics. A section is also included providing New Evidence on the Persecution of Reich, along with an extended bibliography, index and many weblinks for added information. It has many new photos and materials extracted from Dr. DeMeo's publications verifying the reality of the orgone energy, and is a "must have" for all those interested in the issue of life-energy, subtle-energy or energy-medicine research. This is an excellent introduction to a major scientific discovery, organized for the educated layperson but with sufficient detail and citations to stimulate the curiosity of the open-minded physician and scientist.

The normal distribution is widely known and used by scientists and engineers. However, there are many cases when the normal distribution is not appropriate, due to the data being skewed. Rather than leaving you to search through journal articles, advanced theoretical monographs, or introductory texts for alternative distributions, the Handbook of Exponential and Related Distributions for Engineers and Scientists provides a concise, carefully selected presentation of the properties and principles of selected distributions that are most useful for application in the sciences and engineering. The book begins with all the basic mathematical and statistical background necessary to select the correct distribution to model real-world data sets. This includes inference, decision theory, and computational aspects including the popular Bootstrap method. The authors then examine four skewed distributions in detail: exponential, gamma, Weibull, and extreme value. For each one, they discuss general properties and applicability to example data sets, theoretical characterization, estimation of parameters and related inferences, and goodness of fit tests. The final chapter deals with system reliability for series and parallel systems. Presenting methods based on statistical simulations and numerical computations, the Handbook of Exponential and Related Distributions for Engineers and Scientists supplies hands-on tools for applied researchers in need of practical tools for data analysis.