Books by "Brian C. Neumann"
5 books found
"It is not an exaggeration to say that the honey bee is the most well understood insect. We know more about Drosophila genetics, but our integrative understanding of that species pales in comparison to our understanding of every facet of honey bee biology. Despite the tremendous growth in our understanding of honey bee biology, the last comprehensive book on topic was published in 1987. In this book, Brian Johnson offers a comprehensive and up-to-date treatment of honey bee biology. The book covers classic topics such as physiology, communication, division of labor, and reproduction as well as areas that were barely known decades ago such as genomics, cognition, toxicology, and immunity. He concludes with a discussion of honey bees as managed pollinators and conservation issues. Throughout, Johnson also offers his analysis and evaluation of key studies and areas of research. Ultimately, this book is likely to be the new standard reference on honey bee biology and an invaluable resource for anyone with a serious interest in these fascinating organisms"--
R is dynamic, to say the least. More precisely, it is organic, with new functionality and add-on packages appearing constantly. And because of its open-source nature and free availability, R is quickly becoming the software of choice for statistical analysis in a variety of fields. Doing for R what Everitt's other Handbooks have done for S-P
Transcriptional Control of Neural Crest Development
by Brian L. Nelms, Patricia A. Labosky
2010 · Morgan & Claypool Publishers
The neural crest is a remarkable embryonic population of cells found only in vertebrates and has the potential to give rise to many different cell types contributing throughout the body. These derivatives range from the mesenchymal bone and cartilage comprising the facial skeleton, to neuronal derivatives of the peripheral sensory and autonomic nervous systems, to melanocytes throughout the body, and to smooth muscle of the great arteries of the heart. For these cells to correctly progress from an unspecifi ed, nonmigratory population to a wide array of dynamic, differentiated cell types-some of which retain stem cell characteristics presumably to replenish these derivatives-requires a complex network of molecular switches to control the gene programs giving these cells their defi ning structural, enzymatic, migratory, and signaling capacities. This review will bring together current knowledge of neural crest-specifi c transcription factors governing these progressions throughout the course of development. A more thorough understanding of the mechanisms of transcriptional control in differentiation will aid in strategies designed to push undifferentiated cells toward a particular lineage, and unraveling these processes will help toward reprogramming cells from a differentiated to a more naive state. Table of Contents: Introduction / AP Genes / bHLH Genes / ETS Genes / Fox Genes / Homeobox Genes / Hox Genes / Lim Genes / Pax Genes / POU Domain Genes / RAR/RXR Genes / Smad Genes / Sox Genes / Zinc Finger Genes / Other Miscellaneous Genes / References / Author Biographies
The current volume continues the tradition of the Organic Syntheses series, providing carefully checked and edited experimental procedures that describe important synthetic methods, transformations, reagents, and synthetic building blocks or intermediates with demonstrated utility in organic synthesis. These significant and interesting procedures should prove worthwhile to many synthetic chemists working in increasingly diverse areas. A trusted guide for professionals in organic and medicinal chemistry in academia, government, and industries, including pharmaceuticals, fine chemicals, agrochemicals, and biotechnological products.
A Handbook of Statistical Analyses Using R, Second Edition
by Torsten Hothorn, Brian S. Everitt
2009 · CRC Press
A Proven Guide for Easily Using R to Effectively Analyze Data Like its bestselling predecessor, A Handbook of Statistical Analyses Using R, Second Edition provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references. New to the Second Edition New chapters on graphical displays, generalized additive models, and simultaneous inference A new section on generalized linear mixed models that completes the discussion on the analysis of longitudinal data where the response variable does not have a normal distribution New examples and additional exercises in several chapters A new version of the HSAUR package (HSAUR2), which is available from CRAN This edition continues to offer straightforward descriptions of how to conduct a range of statistical analyses using R, from simple inference to recursive partitioning to cluster analysis. Focusing on how to use R and interpret the results, it provides students and researchers in many disciplines with a self-contained means of using R to analyze their data.