The Science of Synthesis Editorial Board, together with the volume editors and authors, is constantly reviewing the whole field of synthetic organic chemistry as presented in Science of Synthesis and evaluating significant developments in synthetic methodology. Several annual volumes updating content across all categories ensure that you always have access to state-of-the-art synthetic methodology.

The widespread use of organoboron compounds justifies the efforts devoted to their synthesis, as well as toward developing an understanding of their reactivity. The nature of the mono- or diboron species is of paramount importance in determining the reversible covalent binding properties of the boron atom with both nucleophiles and electrophiles. By wedding the rich chemical potential of organoboron compounds to the ubiquity of organic scaffolds, advanced borylation reactions have the potential to open unprecedented synthetic alternatives, and new knowledge in the field should encourage chemists to use organoboron compounds. In this volume, the main objective is to provide a collection of the most useful, practical, and reliable methods, reported mainly within the last decade, for boron activation and boron reactivity. The volume covers the main concepts of organoboron compounds and includes experimental procedures, enabling newcomers to the field the instant and reliable application of the new tools in synthesis. Rather than aiming for a comprehensive coverage, the most advanced solutions for challenging transformations are introduced. To this end, a team of pioneers and leaders in the field have been assembled who discuss both the practical and conceptual aspects of this rapidly growing field.

The Science of Synthesis Editorial Board, together with the volume editors and authors, is constantly reviewing the whole field of synthetic organic chemistry as presented in Science of Synthesis and evaluating significant developments in synthetic methodology. Several annual volumes updating content across all categories ensure that you always have access to state-of-the-art synthetic methodology.

The Science of Synthesis Editorial Board, together with the volume editors and authors, is constantly reviewing the whole field of synthetic organic chemistry as presented in Science of Synthesis and evaluating significant developments in synthetic methodology. Several annual volumes updating content across all categories ensure that you always have access to state-of-the-art synthetic methodology.

The Science of Synthesis Editorial Board, together with the volume editors and authors, is constantly reviewing the whole field of synthetic organic chemistry as presented in Science of Synthesis and evaluating significant developments in synthetic methodology. Several annual volumes updating content across all categories ensure that you always have access to state-of-the-art synthetic methodology.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

This open access book synthesizes leading-edge science and management information about forest and rangeland soils of the United States. It offers ways to better understand changing conditions and their impacts on soils, and explores directions that positively affect the future of forest and rangeland soil health. This book outlines soil processes and identifies the research needed to manage forest and rangeland soils in the United States. Chapters give an overview of the state of forest and rangeland soils research in the Nation, including multi-decadal programs (chapter 1), then summarizes various human-caused and natural impacts and their effects on soil carbon, hydrology, biogeochemistry, and biological diversity (chapters 2–5). Other chapters look at the effects of changing conditions on forest soils in wetland and urban settings (chapters 6–7). Impacts include: climate change, severe wildfires, invasive species, pests and diseases, pollution, and land use change. Chapter 8 considers approaches to maintaining or regaining forest and rangeland soil health in the face of these varied impacts. Mapping, monitoring, and data sharing are discussed in chapter 9 as ways to leverage scientific and human resources to address soil health at scales from the landscape to the individual parcel (monitoring networks, data sharing Web sites, and educational soils-centered programs are tabulated in appendix B). Chapter 10 highlights opportunities for deepening our understanding of soils and for sustaining long-term ecosystem health and appendix C summarizes research needs. Nine regional summaries (appendix A) offer a more detailed look at forest and rangeland soils in the United States and its Affiliates.