The material contained in this book originated in interrogations about modern practice in time series analysis. • Why do we use models optimized with respect to one-step ahead foreca- ing performances for applications involving multi-step ahead forecasts? • Why do we infer 'long-term' properties (unit-roots) of an unknown process from statistics essentially based on short-term one-step ahead forecasting performances of particular time series models? • Are we able to detect turning-points of trend components earlier than with traditional signal extraction procedures? The link between 'signal extraction' and the first two questions above is not immediate at first sight. Signal extraction problems are often solved by su- ably designed symmetric filters. Towards the boundaries (t = 1 or t = N) of a time series a particular symmetric filter must be approximated by asymm- ric filters. The time series literature proposes an intuitively straightforward solution for solving this problem: • Stretch the observed time series by forecasts generated by a model. • Apply the symmetric filter to the extended time series. This approach is called 'model-based'. Obviously, the forecast-horizon grows with the length of the symmetric filter. Model-identification and estimation of unknown parameters are then related to the above first two questions. One may further ask, if this approximation problem and the way it is solved by model-based approaches are important topics for practical purposes? Consider some 'prominent' estimation problems: • The determination of the seasonally adjusted actual unemployment rate.
Ideal for both trainees and experienced practitioners, Textbook of Gastrointestinal Radiology, 5th Edition, provides detailed, concise, well-illustrated information on all aspects of GI imaging—now in a single volume for convenient point-of-care reference. Drs. Richard M. Gore and Marc S. Levine lead a team of world-renowned experts to provide unparalleled coverage of all major gastrointestinal disorders as well as the complete scope of abdominal imaging modalities. Every chapter has been thoroughly updated, and new authors provide fresh perspectives on complex imaging topics. - Offers streamlined, actionable content in a new single-volume format for quicker access at the point of care. - Highlights the complete scope of imaging modalities including the latest in MDCT, MRI, diffusion weighted and perfusion imaging, ultrasound, PET/CT, PET/MR, plain radiographs, MRCP, angiography, barium studies, and CT and MR texture analysis of abdominal and pelvic malignancies. - Features more than 1,100 state-of-the art-images, with many in full color. - Discusses the imaging features of abdominal and pelvic malignancies that are key in an era of personalized medicine, as well as the relationship of abdominal and pelvic malignancies to cancer genomics and oncologic mutations that guide novel molecular, targeted and immunotherapies. - Provides a diagnostic approach to incidentally discovered hepatic, pancreatic, and splenic lesions now commonly found on cross-sectional imaging.
This volume presents one of the clinical foundations of vasculopathies: the biological markers and risk factors associated with cardiovascular disease. A detailed biological and clinical framework is provided as a prerequisite for adequate modeling. Chapter 1 presents cardiovascular risk factors and markers, where the search for new criteria is aimed at improving early detection of chronic diseases. The subsequent chapters focus on hypertension, which involves the kidney among other organs as well as many agents, hyperglycemia and diabetes, hyperlipidemias and obesity, and behavior. The last of these risk factors includes altered circadian rhythm, tobacco and alcohol consumption, physical inactivity, and diet. The volumes in this series present all of the data needed at various length scales for a multidisciplinary approach to modeling and simulation of flows in the cardiovascular and ventilatory systems, especially multiscale modeling and coupled simulations. The cardiovascular and respiratory systems are tightly coupled, as their primary function is to supply oxygen to and remove carbon dioxide from the body's cells. Because physiological conduits have deformable and reactive walls, macroscopic flow behavior and prediction must be coupled to nano- and microscopic events in a corrector scheme of regulated mechanisms. Therefore, investigation of flows of blood and air in anatomical conduits requires an understanding of the biology, chemistry, and physics of these systems together with the mathematical tools to describe their functioning in quantitative terms.
Plants depend on physiological mechanisms to combat adverse environmental conditions, such as pathogen attack, wounding, drought, cold, freezing, salt, UV, intense light, heavy metals and SO2. Many of these cause excess production of active oxygen species in plant cells. Plants have evolved complex defense systems against such oxidative stress. The
Unravelling Long COVID An authoritative medical reference on the various ways in which Long-COVID presents and an in-depth discussion of its mechanisms and potential therapeutic options. Unravelling Long COVID aims to provide a better awareness and understanding of the persistent health problems that can arise following SARS-CoV-2 infection. Variously described as Long-COVID, Long-Haulers’ Syndrome, and Post-Acute Sequelae of SARS-CoV-2, this newly-designated disorder is estimated to have affected somewhere between 50 to 250 million people. It is in fact considered by many as the next global public health disaster. With such a broad and important topic, the authors of Unravelling Long COVID have focused primarily on two major problems in the current understanding of Long-COVID: 1.) the failure to distinguish patients with organ damage—here called Long-COVID Disease – and those with unexplained, persistent symptoms—what is termed Long-COVID syndrome, and 2.) the failure of current medical approaches to comprehend and treat those persistent unexplained symptoms. Unravelling Long COVID is: One of the first books focused specifically on defining and understanding Long-COVID with the goal of establishing optimal management A unique reference to distinguish patients with organ damage caused by Long-COVID disease from those with unexplained, persistent symptoms that manifest as Long-COVID syndrome An in-depth exploration of neuroimmune pathways to help clarify the previously unexplained symptoms of Long-COVID Unravelling Long COVID isan essential reference for anyone interested in Long-COVID and the impact that this condition has had on the population. It will be a useful resource for both patients suffering from the Long-Covid syndrome, their physicians and for the growing number of Long-COVID clinics that have been established across the US, the UK, and other countries. This book is paired with a long-COVID blog, updated regularly by the authors, so the reader will be kept up to date with new clinical and research findings, in real time. To visit this site, follow this link: unravellinglongcovid.com – providing the latest information on long-COVID
The material contained in this book originated in interrogations about modern practice in time series analysis. • Why do we use models optimized with respect to one-step ahead foreca- ing performances for applications involving multi-step ahead forecasts? • Why do we infer 'long-term' properties (unit-roots) of an unknown process from statistics essentially based on short-term one-step ahead forecasting performances of particular time series models? • Are we able to detect turning-points of trend components earlier than with traditional signal extraction procedures? The link between 'signal extraction' and the first two questions above is not immediate at first sight. Signal extraction problems are often solved by su- ably designed symmetric filters. Towards the boundaries (t = 1 or t = N) of a time series a particular symmetric filter must be approximated by asymm- ric filters. The time series literature proposes an intuitively straightforward solution for solving this problem: • Stretch the observed time series by forecasts generated by a model. • Apply the symmetric filter to the extended time series. This approach is called 'model-based'. Obviously, the forecast-horizon grows with the length of the symmetric filter. Model-identification and estimation of unknown parameters are then related to the above first two questions. One may further ask, if this approximation problem and the way it is solved by model-based approaches are important topics for practical purposes? Consider some 'prominent' estimation problems: • The determination of the seasonally adjusted actual unemployment rate.
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