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The Food Forum convened a public workshop on February 22-23, 2012, to explore current and emerging knowledge of the human microbiome, its role in human health, its interaction with the diet, and the translation of new research findings into tools and products that improve the nutritional quality of the food supply. The Human Microbiome, Diet, and Health: Workshop Summary summarizes the presentations and discussions that took place during the workshop. Over the two day workshop, several themes covered included: The microbiome is integral to human physiology, health, and disease. The microbiome is arguably the most intimate connection that humans have with their external environment, mostly through diet. Given the emerging nature of research on the microbiome, some important methodology issues might still have to be resolved with respect to undersampling and a lack of causal and mechanistic studies. Dietary interventions intended to have an impact on host biology via their impact on the microbiome are being developed, and the market for these products is seeing tremendous success. However, the current regulatory framework poses challenges to industry interest and investment.
A great number of diverse microorganisms inhabit the human body and are collectively referred to as the human microbiome. Until recently, the role of the human microbiome in maintaining human health was not fully appreciated. Today, however, research is beginning to elucidate associations between perturbations in the human microbiome and human disease and the factors that might be responsible for the perturbations. Studies have indicated that the human microbiome could be affected by environmental chemicals or could modulate exposure to environmental chemicals. Environmental Chemicals, the Human Microbiome, and Health Risk presents a research strategy to improve our understanding of the interactions between environmental chemicals and the human microbiome and the implications of those interactions for human health risk. This report identifies barriers to such research and opportunities for collaboration, highlights key aspects of the human microbiome and its relation to health, describes potential interactions between environmental chemicals and the human microbiome, reviews the risk-assessment framework and reasons for incorporating chemicalâ€"microbiome interactions.
Due to the success of Microbiome and Machine Learning, which collected research results and perspectives of researchers working in the field of machine learning (ML) applied to the analysis of microbiome data, we are launching the second volume to collate any new findings in the field to further our understanding and encourage the participation of experts worldwide in the discussion. The success of ML algorithms in the field is substantially due to their capacity to process high-dimensional data and deal with uncertainty and noise. However, to maximize the combinatory potential of these emerging fields (microbiome and ML), researchers have to deal with some aspects that are complex and inherently related to microbiome data. Microbiome data are convoluted, noisy and highly variable, and non-standard analytical methodologies are required to unlock their clinical and scientific potential. Therefore, although a wide range of statistical modelling and ML methods are available, their application is only sometimes optimal when dealing with microbiome data.
This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.
Nutrition in the Prevention and Treatment of Abdominal Obesity focuses on the important roles that exercise, dietary changes, and foods play in promoting as well as reducing visceral fat. Nutritionists, dieticians, and healthcare providers seeking to address the abdominal obesity epidemic will use this comprehensive resource as a tool in their long-term goal of preventing chronic diseases, especially heart, vascular, and diabetic diseases. Experts from a broad range of disciplines are involved in dealing with the consequences of excessive abdominal fat: cardiology, diabetes research, studies of lipids, endocrinology and metabolism, nutrition, obesity, and exercise physiology. They have contributed chapters that define a range of dietary approaches to reducing risk and associated chronic diseases. They begin by defining visceral obesity and its major outcomes; they also discuss the importance and the challenges of dietary approaches to reduce abdominal obesity, as compared to clinical approaches, with major costs and risks. - Offers detailed, well-documented reviews outlining the various dietary approaches to visceral obesity with their benefits and failures - Includes chapters on types of foods, exercise, and supplements in reducing obesity and its chronic clinical companions, especially diabetes and cardiovascular disease - Helps nutritionists, dieticians, and healthcare providers approach patients in making decision about nutritional therapies and clinical treatments for abdominal obesity, from an evidence-based perspective
Gnotobiotics summarizes and analyzes the research conducted on the use of gnotobiotes, providing detailed information regarding actual facility operation and derivation of gnotobiotic animals. In response to the development of new tools for microbiota and microbiome analysis, the increasing recognition of the various roles of microbiota in health and disease, and the consequent expanding demand for gnotobiotic animals for microbiota/microbiome related research, this volume collates the research of this expanding field into one definitive resource. - Reviews and defines gnotobiotic animal species - Analyzes microbiota in numerous contexts - Presents detailed coverage of the protocols and operation of a gnotobiotic facility
As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates.
Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but