Bread is one of the most fundamental parts of our diet, yet so many of us rely on bland grocery-store offerings when flavorsome breads can easily be made at home. Della Fattoria Bread teaches readers to make the popular breads of this award-winning bakery. More than an instructional guide, the book takes the fear out of bread baking,and encourages bakers to draw on their senses, experiences, and instincts. Weber’s teachings are homespun and based on passed-down wisdom, not on finicky science or dull kitchen textbooks. The book is filled with invaluable bread-baking secrets, including starters and proper techniques, and features recipes for all levels of bakers. Beginners can learn to bake yeasted breads using pans. Advanced bakers can jump right into making free-form loaves of naturally leavened breads in all shapes and flavors. Other chapters include recipes for enriched breads like brioche and challah; pre-fermented breads, including baguettes; and crackers, breadsticks, naan, and more. The book includes recipes that incorporate the breads, too, such as Tomato Bread Soup and Della Fattoria’s famous Tuna Melt Piadina, along with the stories of the bakery and the unique family that has run it for nearly 20 years.

This book constitutes the proceedings of the 27th International Conference on Information Processing in Medical Imaging, IPMI 2021, which was held online during June 28-30, 2021. The conference was originally planned to take place in Bornholm, Denmark, but changed to a virtual format due to the COVID-19 pandemic. The 59 full papers presented in this volume were carefully reviewed and selected from 200 submissions. They were organized in topical sections as follows: registration; causal models and interpretability; generative modelling; shape; brain connectivity; representation learning; segmentation; sequential modelling; learning with few or low quality labels; uncertainty quantification and generative modelling; and deep learning.

This book constitutes the refereed proceedings of the Third International Workshop on Connectomics in NeuroImaging, CNI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 13 full papers presented were carefully reviewed and selected from 14 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.

This book provides a comprehensive overview of the use of PET and SPECT in not only classic neurodegenerative disorders but also cerebrovascular disorders, brain tumors, epilepsy, head trauma, coma, sleeping disorders, and inflammatory and infectious diseases of the CNS. The new edition has been revised and updated to reflect recent advances and includes additional chapters, for example on the use of artificial intelligence and machine learning in imaging data analysis, the study of brain connectivity using PET and SPECT images, and the role of PET imaging in modulation of brain functioning by deep brain stimulation. The authors are renowned experts whose dedication to the investigation of neurological disorders through nuclear medicine technology has achieved international recognition. Most chapters are written jointly by a clinical neurologist and a nuclear medicine specialist to ensure a multidisciplinary approach. This state of the art compendium will be invaluable for neurologists and radiologists/nuclear medicine specialists and will also be informative for interested general practitioners and geriatricians. Companion volumes on PET and SPECT in psychiatry and in neurobiological systems complete a trilogy.

In an attempt to instil trust in their performance, credibility, integrity, efficiency, cost-effectiveness, and good governance, many public organizations are in effect viewing tax-paying citizens as consumers. Little research exists to explore synergies between the market economy, public administration reformation, and their complex bilateral effects. This book takes a timely look at the heightened need for public administration reform as a result of the economic challenges currently faced by nations across the globe. In particular it explores the roles of eGovernment and a citizen-centric focus in this transformation. Public Administration Reform examines several commonly-held assumptions about public administration: the public sector is slow and bureaucratic; government employees are frequently disengaged; and government agencies are sometimes wasteful. eGovernment is proposed as a key tool in the improvement of both public services and reputations of public organizations.

Sponsored by the Association for Education Finance and Policy (AEFP), this groundbreaking new handbook assembles in one place the existing research-based knowledge in education finance and policy, thereby helping to define this evolving field of research and practice. It provides a readily available resource for anyone seriously involved in education finance and policy in the United States and around the world. The Handbook traces the evolution of the field from its initial focus on school inputs and the revenue sources used to finance these inputs to a focus on educational outcomes and the larger policies used to achieve them. It shows how the current decision-making context in school finance inevitably interacts with those of governance, accountability, equity, privatization, and other areas of education policy. Because a full understanding of the important contemporary issues requires input from a variety of perspectives, the Handbook draws on contributors from a variety of disciplines. While many of the chapters cover complex state-of-the-art empirical research, the authors explain key concepts in language that non-specialists can understand.

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.