Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them. Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an introduction to the problem using the "cocktail-party" analogy, Part I provides the statistical background needed for the Bayesian source separation model. Part II considers the instantaneous constant mixing models, where the observed vectors and unobserved sources are independent over time but allowed to be dependent within each vector. Part III details more general models in which sources can be delayed, mixing coefficients can change over time, and observation and source vectors can be correlated over time. For each model discussed, the author gives two distinct ways to estimate the parameters. Real-world source separation problems, encountered in disciplines from engineering and computer science to economics and image processing, are more difficult than they appear. This book furnishes the fundamental statistical material and up-to-date research results that enable readers to understand and apply Bayesian methods to help solve the many "cocktail party" problems they may confront in practice.

Iron Acquisition by the Genus Mycobacterium summarizes the early evidence for the necessity of iron in mycobacteria and the discovery of the mycobacterial siderophores mycobactin, carboxymycobactin, and exochelin. The structural characterization of the mycobacterial siderophores is described. The genes so far identified as essential for iron acquisition and maintenance of an infection by pathogenic mycobacteria are discussed. The potential role of siderocalin in iron gathering by M. tuberculosis is featured. Because new drugs for M. tuberculosis are needed, this brief also emphasizes the design of antibiotics that interfere with siderophore biosynthesis and the use of siderophore analogs and/or conjugates.

Emergency physicians assess and manage a wide variety of problems from patients presenting with a diversity of severities, ranging from mild to severe and life-threatening. They are expected to maintain their competency and expertise in areas where there is rapid knowledge change. Evidence-based Emergency Medicine is the first book of its kind in emergency medicine to tackle the problems practicing physicians encounter in the emergency setting using an evidence-based approach. It summarizes the published evidence available for the diagnosis and treatment of common emergency health care problems in adults. Each chapter contextualizes a topic area using a clinical vignette and generates a series of key clinically important diagnostic and treatment questions. By completing detailed reviews of diagnostic and treatment research, using evidence from systematic reviews, RCTs, and prospective observational studies, the authors provide conclusions and practical recommendations. Focusing primarily on diagnosis in areas where evidence for treatment is well accepted (e.g. DVTs), and treatment in other diseases where diagnosis is not complex (e.g. asthma), this text is written by leading emergency physicians at the forefront of evidence-based medicine. Evidence-based Emergency Medicine is ideal for emergency physicians and trainees, emergency department staff, and family physicians specialising in the acute care of medical and injured patients.

Virginia Polytechnic Institute, Engineering Experiment Station, V31, No. 3, January, 1938.

Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them.

Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an introduction to the problem using the "cocktail-party" analogy, Part I provides the statistical background needed for the Bayesian source separation model. Part II considers the instantaneous constant mixing models, where the observed vectors and unobserved sources are independent over time but allowed to be dependent within each vector. Part III details more general models in which sources can be delayed, mixing coefficients can change over time, and observation and source vectors can be correlated over time. For each model discussed, the author gives two distinct ways to estimate the parameters.

Real-world source separation problems, encountered in disciplines from engineering and computer science to economics and image processing, are more difficult than they appear. This book furnishes the fundamental statistical material and up-to-date research results that enable readers to understand and apply Bayesian methods to help solve the many "cocktail party" problems they may confront in practice.