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This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.
Provides exceptional insights and clarity to patterns of order in living things, including the promise of healing and new birth in Christ.
Authoritative, thorough, and engaging, Life: The Science of Biology achieves an optimal balance of scholarship and teachability, never losing sight of either the science or the student. The first introductory text to present biological concepts through the research that revealed them, Life covers the full range of topics with an integrated experimental focus that flows naturally from the narrative. This approach helps to bring the drama of classic and cutting-edge research to the classroom - but always in the context of reinforcing core ideas and the innovative scientific thinking behind them. Students will experience biology not just as a litany of facts or a highlight reel of experiments, but as a rich, coherent discipline.
A revealing and provocative look at the current state of global science We take the advance of science as given. But how does science really work? Is it truly as healthy as we tend to think? How does the system itself shape what scientists do? The Secret Life of Science takes a clear-eyed and provocative look at the current state of global science, shedding light on a cutthroat and tightly tensioned enterprise that even scientists themselves often don't fully understand. The Secret Life of Science is a dispatch from the front lines of modern science. It paints a startling picture of a complex scientific ecosystem that has become the most competitive free-market environment on the planet. It reveals how big this ecosystem really is, what motivates its participants, and who reaps the rewards. Are there too few scientists in the world or too many? Are some fields expanding at the expense of others? What science is shared or published, and who determines what the public gets to hear about? What is the future of science? Answering these and other questions, this controversial book explains why globalization is not necessarily good for science, nor is the continued growth in the number of scientists. It portrays a scientific community engaged in a race for limited resources that determines whether careers are lost or won, whose research visions become the mainstream, and whose vested interests end up in control. The Secret Life of Science explains why this hypercompetitive environment is stifling the diversity of research and the resiliency of science itself, and why new ideas are needed to ensure that the scientific enterprise remains healthy and vibrant.
Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
Does nature have intrinsic value? Should we be doing more to save wilderness and ocean ecosystems? What are our duties to future generations of humans? Do animals have rights? This revised edition of "Life Science Ethics" introduces these questions using narrative case studies on genetically modified foods, use of animals in research, nanotechnology, and global climate change, and then explores them in detail using essays written by nationally-recognized experts in the ethics field. Part I introduces ethics, the relationship of religion to ethics, how we assess ethical arguments, and a method ethicists use to reason about ethical theories. Part II demonstrates the relevance of ethical reasoning to the environment, land, farms, food, biotechnology, genetically modified foods, animals in agriculture and research, climate change, and nanotechnology. Part III presents case studies for the topics found in Part II.
A comprehensive text designed to give the educator material to reinforce relevant scientific information. Provide students with a knowledge base that meets the common core standards.
Freshman and sophomore life sciences students respond well to the modeling approach to calculus, difference equations, and differential equations presented in this book. Examples of population dynamics, pharmacokinetics, and biologically relevant physical processes are introduced in Chapter 1, and these and other life sciences topics are developed throughout the text. The students should have studied algebra, geometry, and trigonometry, but may be life sciences students because they have not enjoyed their previous mathematics courses.