This detailed volume focuses on the CRISPR-associated guide RNA and how it can be designed, modified, and validated for a broad repertoire of purposes. Beginning with a section on computational design of target-specific guide RNAs, the book continues by covering chemical modifications to alter guide RNA stability, specificity, and efficiency, as well as to create inducible guide RNAs, append additional functional domains, and express guide RNAs in a conditional manner. It concludes with methods for measuring off-target guide RNA activity. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and essential, CRISPR Guide RNA Design: Methods and Protocols provides a comprehensive pipeline for guide RNA design and aims to be an invaluable resource in applying this powerful technology to basic research and therapeutic applications.

This book highlights the latest advances in functional micro/nano imaging probes and their applications for biomedical imaging and therapy. Given the rapid emergence of transdisciplinary research and applications in materials, chemical probes and translational medicine in recent years, scientists in these areas are expected to keep up to date on the latest technologies and advances to promote comprehensive innovations. Addressing this need, the book presents recently introduced features, emerging techniques, and new strategies, complemented by detailed illustrations. Covering the status quo and offering an outlook on the future, it benefits all readers with an interest in functional materials, especially micro/nano imaging materials for biomedical imaging applications, providing them with both vital updates and inspiration for their own research.

This volume is the third in a series entitled" Advanced Methods of Physiological System Modeling" and the fifth in a series of research volumes published by Plenum under the sponsorship of the Biomedical Simulations Resource (BMSR) at the Uni versity of Southern California in the context of dissemination activities supported by the Biomedical Research Technology Program of the National Center for Research Resources at the National Institutes of Health under Grant No. P41 RR-OI861. These volumes are edited by BMSR principal scientists and report on recent research de velopments in the area of physiological systems modeling, as well as on advanced methods for analysis of physiological signals and data. As in the previous two volumes of this series, the work reported herein is con cerned with the development of advanced modeling methodologies and their novel application to problems of biomedical interest, with emphasis on nonlinear aspects of physiological function. The term "advanced methodologies" is used to indicate that the scope of this work extends beyond the ordinary type of analysis, which is confined traditionally to the linear domain. As the importance of nonlinearities in understanding the complex mechanisms of physiological function is increasingly recognized, the need for effective and practical modeling methodologies that address the issue of nonlinear dynamics in life sciences becomes more and more pressing."

This book comprises select peer-reviewed proceedings of the medical challenge - C-NMC challenge: Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. The challenge was run as part of the IEEE International Symposium on Biomedical Imaging (IEEE ISBI) 2019 held at Venice, Italy in April 2019. Cell classification via image processing has recently gained interest from the point of view of building computer-assisted diagnostic tools for blood disorders such as leukaemia. In order to arrive at a conclusive decision on disease diagnosis and degree of progression, it is very important to identify malignant cells with high accuracy. Computer-assisted tools can be very helpful in automating the process of cell segmentation and identification because morphologically both cell types appear similar. This particular challenge was run on a curated data set of more than 14000 cell images of very high quality. More than 200 international teams participated in the challenge. This book covers various solutions using machine learning and deep learning approaches. The book will prove useful for academics, researchers, and professionals interested in building low-cost automated diagnostic tools for cancer diagnosis and treatment.

Thermal Ablation Therapy: Theory and Simulation includes detailed theoretical and technical concepts of thermal ablation therapy in different body organs. Concepts of ablation technology based on different thermal ablation methods are introduced, along with changes in the tissues' mechanical properties due to thermal denaturation. The book emphasizes the mathematical and engineering concepts of RF and MW energy propagation through tissues and where high heating rates produced by MW systems can overcome the heat-sink effects from nearby vessels. The design and tuning of the MW antennas to deliver energy efficiently to specific organ systems such as the liver or lung is also covered. Other sections cover the computational modeling of radiofrequency ablation and microwave ablation procedures for developing and implementing new efficient ablation in clinical systems, numerical simulations for different scenarios of different organs with different size using RF and MW ablation systems with different antennas'/probes design and configurations, and numerical techniques for temperature profile in tissues.

Autonomous Robot-Aided Optical Manipulation for Biological Cells gives a systematically and almost self-contained description of the many facets of modeling, sensing, and control techniques or experimentally exploring emerging trends in optical manipulation of biological cell in micro/nanorobotics systems. To achieve biomedical applications, reliability design, modeling, and precision control are vitally important for the development of engineering systems. With the advances in modeling, sensing, and control techniques, it is opportunistic to exploit them for the benefit of reliability design, actuation, and precision control of micro/nanomanipulation systems to expanding the applications of robot at the micro and nano scales, especially in biomedical engineering. This book presents new techniques in reliability modeling and advanced control of robot-aided optical manipulation of biological cells systems. The book will be beneficial to the researchers within robotics, mechatronics, biomedical engineering, and automatic control society, including both academic and industrial parts.

This book introduces a variety of well-proven and newly developed nature-inspired optimization algorithms solving a wide range of real-life biomedical and healthcare problems. Few solo and hybrid approaches are demonstrated in a lucid manner for the effective integration and finding solution for a large-scale complex healthcare problem. In the present bigdata-based computing scenario, nature-inspired optimization techniques present adaptive mechanisms that permit the understanding of complex data and altering environments. This book is a voluminous collection for the confront faced by the healthcare institutions and hospitals for practical analysis, storage, and data analysis. It explores the distinct nature-inspired optimization-based approaches that are able to handle more accurate outcomes for the current biomedical and healthcare problems. In addition to providing a state-of-the-art and advanced intelligent methods, it also enlightens an insight for solving diversified healthcare problems such as cancer and diabetes.

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.

The vision of seamless human-robot interaction in our everyday life that allows for tight cooperation between human and robot has not become reality yet. However, the recent increase in technology maturity finally made it possible to realize systems of high integration, advanced sensorial capabilities and enhanced power to cross this barrier and merge living spaces of humans and robot workspaces to at least a certain extent. Together with the increasing industrial effort to realize first commercial service robotics products this makes it necessary to properly address one of the most fundamental questions of Human-Robot Interaction: How to ensure safety in human-robot coexistence? In this authoritative monograph, the essential question about the necessary requirements for a safe robot is addressed in depth and from various perspectives. The approach taken in this book focuses on the biomechanical level of injury assessment, addresses the physical evaluation of robot-human impacts, and isolates the major factors that cause human injuries. This assessment is the basis for the design and exploration of various measures to improve safety in human-robot interaction. They range from control schemes for collision detection, reflex reaction, and avoidance to the investigation of novel joint designs that equip robots with fundamentally new capabilities. By the depth of its analysis and exceptionally salient experimental work, this monograph offers one of the most comprehensive treatments of the safety challenge in the field.

Signal Processing in Medicine and Biology: Innovations in Big Data Processing provides an interdisciplinary look at state-of-the-art innovations in biomedical signal processing, especially as it applies to large data sets and machine learning. Chapters are presented with detailed mathematics and complete implementation specifics so that readers can completely master these techniques. The book presents tutorials and examples of successful applications and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology at the intersection between healthcare, engineering, and computer science.

This thesis advances the long-standing challenge of measuring oxidative stress and deciphering its underlying mechanisms, and also outlines the advantages and limitations of existing design strategies. It presents a range of approaches for the chemical synthesis of fluorescent probes that detect reversible changes in cellular oxidative stress. The ability to visualise cellular processes in real-time is crucial to understanding disease development and streamline treatment, and this can be achieved using fluorescent tools that can sense reversible disturbances in cellular environments during pathogenesis. The perturbations in cellular redox state are of particular current interest in medical research, since oxidative stress is implicated in the pathogenesis of a number of diseases. The book investigates different strategies used to achieve ratiometric fluorescence output of the reversible redox probes, which nullify concentration effects associated with intensity-based probes. It also describes suitable approaches to target these probes to specific cellular organelles, thereby enabling medical researchers to visualise sub-cellular oxidative stress levels, and addressing the typically poor uptake of chemical tools into biological studies. In total it reports on four new probes that are now being used by over twenty research groups around the globe, and two of which have been commercialised. The final chapters of this thesis demonstrate successful applications of the sensors in a variety of biological systems ranging from prokaryotes to mammalian cells and whole organisms. The results described clearly indicate the immense value of collaborative, cross-disciplinary research.

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks.

This book covers the development of biotechnology based on carbon nanostructures, with a focus on nanotubes, addressing also fullerenes and amorphous carbons. The book is divided into 7 chapters, addressing tissue engineering, genetic engineering and therapy, as well as the environmental and health impacts of carbon nanostructures.

This book provides a comprehensive overview of the design, generation and characterization of minimal cell systems. Written by leading experts, it presents an in-depth analysis of the current issues and challenges in the field, including recent advances in the generation and characterization of reduced-genome strains generated from model organisms with relevance in biotechnology, and basic research such as Escherichia coli, Corynebacterium glutamicum and yeast. It also discusses methodologies, such as bottom-up and top-down genome minimization strategies, as well as novel analytical and experimental approaches to characterize and generate minimal cells. Lastly, it presents the latest research related to minimal cells of serveral microorganisms, e.g. Bacillus subtilis. The design of biological systems for biotechnological purposes employs strategies aimed at optimizing specific tasks. This approach is based on enhancing certain biological functions while reducing other capacities that are not required or that could be detrimental to the desired objective. A highly optimized cell factory would be expected to have only the capacity for reproduction and for performing the expected task. Such a hypothetical organism would be considered a minimal cell. At present, numerous research groups in academia and industry are exploring the theoretical and practical implications of constructing and using minimal cells and are providing valuable fundamental insights into the characteristics of minimal genomes, leading to an understanding of the essential gene set. In addition, research in this field is providing valuable information on the physiology of minimal cells and their utilization as a biological chassis to which useful biotechnological functions can be added.

Structural Biomaterials: Properties, Characteristics, and Selection serves as a single point of reference to digest current research and develop a deeper understanding in the field of biomaterials engineering. This book uses a materials-focused approach, allowing the reader to quickly access specific, detailed information on biomaterials characterization and selection. Relevant to a range of readers, this book provides holistic coverage of the broad categories of structural biomaterials currently available and used in medical applications, highlighting the property requirements for structural biomaterials, their biocompatibility performance and their safety regulation in key categories such as metals, ceramics and polymers. The materials science perspective of this text ensures the content is accessible even to those without an extensive background in applied medicine, positioning this text not just for students, but as an overview and reference for researchers, scientists and engineers entering the field from related materials science disciplines.

Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson's and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks.

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques. Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis.

This book focuses on current applications of glial cells in neural regeneration, especially in spinal cord repair. It introduces the application of a few types of glial cells including oligodendrocyte, astrocyte, Schwann cells, and stem cell derived glial cells in neural regeneration. The latest glial cell research with biomaterials, gene modification, and electrical signals is also summarized. This is an ideal book for undergraduate and research students in tissue engineering, neurobiology, and regenerative medicine as well as researchers in the field.

Explores the latest advances in virus particle assembly Details the different mechanisms biological motors use to move on their diverse substrates Demonstrates how knowledge of fundamental processes have been used to advance bio-nanotechnology Discusses the latest advances in DNA and RNA nanoparticle assembly and use Introduces the use of DNA/RNA nanoparticles for drug delivery

Features • Discusses novel methods of cancer diagnostics and cancer treatment. • Details non and minimally-invasive photonics techniques. • Explores the applications of machine learning and artificial intelligence to these novel techniques.

This book discusses computer-supported medical diagnosis with a particular focus on ovarian tumor diagnosis - since ovarian cancer is difficult to diagnose and has high mortality rates, especially in Central and Eastern Europe. It presents the theoretical foundations (both medical and mathematical) of the intelligent OvaExpert system, which supports decision-making in tumor diagnosis. OvaExpert was created primarily to help gynecologists predict the malignancy of ovarian tumors by applying the existing diagnostic models and using modern methods of computational intelligence that accommodate imprecise and imperfect medical data, both of which are common features of everyday medical practice. The book presents novel methods based on interval-valued fuzzy sets and the theory of their cardinalities.

This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to learn the core principles of biomedical signal analysis, algorithms, and applications, while also offering a valuable reference work for biomedical engineers and clinicians who wish to learn more about the theory and recent applications of neural engineering and biomedical signal processing.

Advances in Polymeric Nanomaterials for Biomedical Applications examines advanced polymer synthetic strategies for developing novel biomaterials for use in medicine. With a strong focus on fundamentals and structure, the authors also explore their fabrication, along with their current and potential biomedical applications. The book begins with a look at the fundamentals of polymeric nanomaterials and their properties and then discusses the design of biomaterials and their applications in drug delivery. Further chapters explore important applications, such as imaging and regenerative medicine, including current challenges and future trends. This valuable resource is especially useful for materials and polymer scientists, and bioengineers wishing to broaden their knowledge of polymeric nanobiomaterials.

This book introduces modeling and simulation of linear time invariant systems and demonstrates how these translate to systems engineering, mechatronics engineering, and biomedical engineering. It is organized into nine chapters that follow the lectures used for a one-semester course on this topic, making it appropriate for students as well as researchers. The author discusses state space modeling derived from two modeling techniques and the analysis of the system and usage of modeling in control systems design. It also contains a unique chapter on multidisciplinary energy systems with a special focus on bioengineering systems and expands upon how the bond graph augments research in biomedical and bio-mechatronics systems.

The book Intelligent Healthcare: Infrastructure, Algorithms, and Management (R) cover a wide range of research topics on innovative intelligent healthcare solutions and advancements with the latest research developments. Data analytics are relevant for healthcare to meet many technical challenges and issues that need to be addressed to realize this potential. The advanced healthcare systems have to be upgraded with new capabilities such as data analytics, machine learning, intelligent decision making, and more professional services. The Internet of Things helps to design and develop intelligent healthcare solutions assisted by security, data analytics, and machine learning.This book will provide federated learning, Data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart healthcare. This book aims to attract works on multidisciplinary research spanning across computer science and engineering, environmental studies, services, urban planning and development, Healthcare, social sciences, and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative learning and computing solutions and big medical data-powered applications to cope with the real-world challenges for building smart healthcare sectors. Main Features: O Immersive technologies in healthcare O Internet of medical things O Federated learning algorithms O Explainable AI in Pervasive Healthcare O New management principles using biomedical data O Secured healthcare management systems This book aims to set up a better understanding of data scientists, researchers, and technologists under innovative digital health. The reader can find out existing research challenges, current market trends, and low-cost technologies to smoothly address the digital health issue.