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Vision is the process of extracting behaviorally-relevant information from patterns of light that fall on retina as the eyes sample the outside world. Traditionally, nonhuman primates (macaque monkeys, in particular) have been viewed by many as the animal model-of-choice for investigating the neuronal substrates of visual processing, not only because their visual systems closely mirror our own, but also because it is often assumed that “simpler” brains lack advanced visual processing machinery. However, this narrow view of visual neuroscience ignores the fact that vision is widely distributed throughout the animal kingdom, enabling a wide repertoire of complex behaviors in species from insects to birds, fish, and mammals. Recent years have seen a resurgence of interest in alternative animal models for vision research, especially rodents. This resurgence is partly due to the availability of increasingly powerful experimental approaches (e.g., optogenetics and two-photon imaging) that are challenging to apply to their full potential in primates. Meanwhile, even more phylogenetically distant species such as birds, fish, and insects have long been workhorse animal models for gaining insight into the core computations underlying visual processing. In many cases, these animal models are valuable precisely because their visual systems are simpler than the primate visual system. Simpler systems are often easier to understand, and studying a diversity of neuronal systems that achieve similar functions can focus attention on those computational principles that are universal and essential. This Research Topic provides a survey of the state of the art in the use of animal models of visual functions that are alternative to macaques. It includes original research, methods articles, reviews, and opinions that exploit a variety of animal models (including rodents, birds, fishes and insects, as well as small New World monkey, the marmoset) to investigate visual function. The experimental approaches covered by these studies range from psychophysics and electrophysiology to histology and genetics, testifying to the richness and depth of visual neuroscience in non-macaque species.
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This is the most comprehensive dictionary of maintenance and reliability terms ever compiled, covering the process, manufacturing, and other related industries, every major area of engineering used in industry, and more. The over 15,000 entries are all alphabetically arranged and include special features to encourage usage and understanding. They are supplemented by hundreds of figures and tables that clearly demonstrate the principles & concepts behind important process control, instrumentation, reliability, machinery, asset management, lubrication, corrosion, and much much more. With contributions by leading researchers in the field: Zaki Yamani Bin Zakaria Department, Chemical Engineering, Faculty Universiti Teknologi Malaysia, Malaysia Prof. Jelenka B. Savkovic-Stevanovic, Chemical Engineering Dept, University of Belgrade, Serbia Jim Drago, PE, Garlock an EnPro Industries family of companies, USA Robert Perez, President of Pumpcalcs, USA Luiz Alberto Verri, Independent Consultatnt, Verri Veritatis Consultoria, Brasil Matt Tones, Garlock an EnPro Industries family of companies, USA Dr. Reza Javaherdashti, formerly with Qatar University, Doha-Qatar Prof. Semra Bilgic, Faculty of Sciences, Department of Physical Chemistry, Ankara University, Turkey Dr. Mazura Jusoh , Chemical Engineering Department, Universiti Teknologi Malaysia Jayesh Ramesh Tekchandaney, Unique Mixers and Furnaces Pvt. Ltd. Dr. Henry Tan, Senior Lecturer in Safety & Reliability Engineering, and Subsea Engineering, School of Engineering, University of Aberdeen Fiddoson Fiddo, School of Engineering, University of Aberdeen Prof. Roy Johnsen, NTNU, Norway Prof. N. Sitaram , Thermal Turbomachines Laboratory, Department of Mechanical Engineering, IIT Madras, Chennai India Ghazaleh Mohammadali, IranOilGas Network Members' Services Greg Livelli, ABB Instrumentation, Warminster, Pennsylvania, USA Gas Processors Suppliers Association (GPSA)
This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies.
From a journalist and former lab researcher, a penetrating investigation of the explosion in cases of scientific fraud and the factors behind it. In the 1970s, a scientific scandal about painted mice hit the headlines. A cancer researcher was found to have deliberately falsified his experiments by coloring transplanted mouse skin with ink. This widely publicized case of scientific misconduct marked the beginning of an epidemic of fraud that plagues the scientific community today. From manipulated results and made-up data to retouched illustrations and plagiarism, cases of scientific fraud have skyrocketed in the past two decades, especially in the biomedical sciences. Fraud in the Lab examines cases of scientific misconduct around the world and asks why this behavior is so pervasive. Nicolas Chevassus-au-Louis points to large-scale trends that have led to an environment of heightened competition, extreme self-interest, and emphasis on short-term payoffs. Because of the move toward highly specialized research, fewer experts are qualified to verify experimental findings. And the pace of journal publishing has exacerbated the scientific rewards system—publish or perish holds sway more than ever. Even when instances of misconduct are discovered, researchers often face few consequences, and falsified data may continue to circulate after an article has been retracted. Sharp and damning, this exposé details the circumstances that have allowed scientific standards to decline. Fraud in the Lab reveals the intense social pressures that lead to fraud, documents the lasting impact it has had on the scientific community, and highlights recent initiatives and proposals to reduce the extent of misconduct in the future.