The series Advances in Stem Cell Biology is a timely and expansive collection of comprehensive information and new discoveries in the field of stem cell biology. iPSCs for Modeling Central Nervous System Disorders, Volume 6 addresses how induced pluripotent stem cells can be used to model various CNS disorders. Somatic cells can be reprogrammed into Induced pluripotent stem cells by the expression of specific transcription factors. These cells are transforming biomedical research in the last 15 years. The volume teaches readers about current advances in the field. This book describes the use of induced pluripotent stem cells to model several CNS diseases in vitro, enabling us to study the cellular and molecular mechanisms involved in different CNS pathologies. Further insights into these mechanisms will have important implications for our understanding of CNS disease appearance, development, and progression. In recent years, remarkable progress has been made in the obtention of induced pluripotent stem cells and their differentiation into several cell types, tissues and organs using state-of-art techniques. These advantages facilitated identification of key targets and definition of the molecular basis of several CNS disorders. This volume will cover what we know so far about the use of iPSCs to model different CNS disorders, such as: Alzheimer's disease, Autism, Amyotrophic Lateral Sclerosis, Schizophrenia, Fragile X Syndrome, Spinal Muscular Atrophy, Rett Syndrome, Angelman syndrome, Parkinson`s Disease, Leber Hereditary Optic Neuropathy, Anorexia Nervosa, and more. The volume is written for researchers and scientists interested in stem cell therapy, cell biology, regenerative medicine, and neuroscience; and is contributed by world-renowned authors in the field.Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs. This book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry.Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation.An evidence-based resilience toolkit to help you find better, more sustainable ways to succeed at work and lifeIn The Burnout Fix, the award-winning psychologist and board-certified leadership coach Dr. Jacinta M. Jimenez shows you how to harness science-backed resilience strategies to survive, and thrive, in today's "always on, always connected" world-where a reported 60% of employees report being stressed out all or most of the time at work.Packed with compelling, real-world stories from years of coaching and the latest research in positive, social, and motivational psychology, The Burnout Fix shows how neglecting to nurture your personal pulse can undermine all your efforts at working harder and "smarter." You'll learn how integrate healthy personal "PULSE" practices into all aspects of your life, from pacing for performance and leveraging leisure time to securing a support system and evaluating how to regain control of your time and priorities. Whether you are an individual who wishes to build out a set of lasting resilience capabilities, a leader dedicated to keeping your team or organization engaged and flourishing, The Burnout Fix will reshape the way you think about success while giving you-and your people-the tools and strategies you need to thrive.
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