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.

Practicing scientists know that the quality of their livelihood is strongly connected to the quality of their writing, and critical thinking is the most necessary and valuable tool for effectively generating and communicating scientific information. Writing in the Life Sciences is an innovative, process-based text that gives beginning writers the tools to write about science skillfully by taking a critical thinking approach. Laurence Greene emphasizes "writing as thinking" as he takes beginning writers through the important stages of planning, drafting, and revising their work. Throughout, he uses focused and systematic critical reading and thinking activities to help scientific writers develop the skills to effectively communicate. Each chapter addresses a particular writing task rather than a specific type of document. The book makes clear which tasks are important for all writing projects (i.e., audience analysis, attending to instructions) and which are unique to a specific writing project (rhetorical goals for each type of document). Ideal for Scientific Writing courses and writing-intensive courses in various science departments (e.g., Biology, Environmental Studies, etc.), this innovative, process-based text goes beyond explaining what scientific writing is and gives students the tools to do it skillfully.

The second half of the twentieth century brought extraordinary transformations in knowledge and practice of the life sciences. In an era of decolonization, mass social welfare policies, and the formation of new international institutions such as UNESCO and the WHO, monumental advances were made in both theoretical and practical applications of the life sciences, including the discovery of life’s molecular processes and substantive improvements in global public health and medicine. Combining perspectives from the history of science and world history, this volume examines the impact of major world-historical processes of the postwar period on the evolution of the life sciences. Contributors consider the long-term evolution of scientific practice, research, and innovation across a range of fields and subfields in the life sciences, and in the context of Cold War anxieties and ambitions. Together, they examine how the formation of international organizations and global research programs allowed for transnational exchange and cooperation, but in a period rife with competition and nationalist interests, which influenced dramatic changes in the field as the postcolonial world order unfolded.

Each chapter has three types of learning aides for students: open-ended questions, multiple-choice questions, and quantitative problems. There is an average of about 50 per chapter. There are also a number of worked examples in the chapters, averaging over 5 per chapter, and almost 600 photos and line drawings.

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

The potential misuse of advances in life sciences research is raising concerns about national security threats. Dual Use Research of Concern in the Life Sciences: Current Issues and Controversies examines the U.S. strategy for reducing biosecurity risks in life sciences research and considers mechanisms that would allow researchers to manage the dissemination of the results of research while mitigating the potential for harm to national security.

Model formulae represent a powerful methodology for describing, discussing, understanding, and performing that large part of statistical tests known as linear statistics. The book aims to put this methodology firmly within the grasp of undergraduates.

Broad perspective on collectivity in the life sciences, from microorganisms to human consensus, and the theoretical and empirical opportunities and challenges. Many researchers and scholars in the life sciences have become increasingly critical of the traditional methodological focus on the individual. This volume counters such methodological individualism by exploring recent and influential work in the life sciences that utilizes notions of collectivity, sociality, rich interactions, and emergent phenomena as essential explanatory tools to handle numerous persistent scientific questions in the life sciences. The contributors consider case studies of collectivity that range from microorganisms to human consensus, discussing theoretical and empirical challenges and the innovative methods and solutions scientists have devised. The contributors offer historical, philosophical, and biological perspectives on collectivity, and describe collective phenomena seen in insects, the immune system, communication, and human collectivity, with examples ranging from cooperative transport in the longhorn crazy ant to the evolution of autobiographical memory. They examine ways of explaining collectivity, including case studies and modeling approaches, and explore collectivity's explanatory power. They present a comprehensive look at a specific case of collectivity: the Holobiont notion (the idea of a multi-species collective, a host and diverse microorganisms) and the hologenome theory (which posits that the holobiont and its hologenome are a unit of adaption). The volume concludes with reflections on the work of the late physicist Eshel Ben-Jacob, pioneer in the study of collective phenomena in living systems. Contributors Oren Bader, John Beatty, Dinah R. Davison, Daniel Dor, Ofer Feinerman, Raghavendra Gadagkar, Scott F. Gilbert, Snait B. Gissis, Deborah M. Gordon, James Griesemer, Zachariah I. Grochau-Wright, Erik R. Hanschen, Eva Jablonka, Mohit Kumar Jolly, Anat Kolumbus, Ehud Lamm, Herbert Levine, Arnon Levy, Xue-Fei Li, Elisabeth A. Lloyd, Yael Lubin, Eva Maria Luef, Ehud Meron, Richard E. Michod, Samir Okasha, Simone Pika, Joan Roughgarden, Eugene Rosenberg, Ayelet Shavit, Yael Silver, Alfred I. Tauber, Ilana Zilber-Rosenberg

A clear and concise survey of the major themes and theories embedded in the history of life science, this book covers the development and significance of scientific methodologies, the relationship between science and society, and the diverse ideologies and current paradigms affecting the evolution and progression of biological studies. The author discusses cell theory, embryology, physiology, microbiology, evolution, genetics, and molecular biology; the Human Genome Project; and genomics and proteomics. Covering the philosophies of ancient civilizations to modern advances in genomics and molecular biology, the book is a unique and comprehensive resource.

Introduces the diverse roles metaphors play in the life sciences and highlights their significance for theory, communication, and education.