Reproduction and fetal development in humans and experimental animals may be adversely affected by a wide range of drugs and environmental agents. They may be toxic in the parental generation leading to impaired germ cell formation, loss of reproductive poten tial and infertility following chronic exposure. Deviant fetal development occurs as a consequence of cellular damage at sensi tive stages in development. Changes seen range from intrauterine mortality and structural malformation to growth retardation with physiological and behavioural defects. Concepts on the mechanism of action of teratogens are discussed with reference to some better-known agents. Presently, regulatory authorities prefer whole animal studies in predictive safety evaluation of substances to which a pregnant woman may be exposed. Tests are conducted over one or more generations and are designed to study the influence of test com pounds upon general reproductive performance, fertility, fetal development and perinatal and postnatal behaviour. Experiments are designed to permit compounds to be tested under conditions resembling expected human exposure. Small rodents and rabbits are preferred species on account of their convenience as laboratory animals and the available knowledge regarding their reproductive behaviour, fetal development and known sensitivity to human teratogens.

A. N. WORDEN, D. V. PARKE AND J. MARKS THE BACKGROUND There is nothing new about the fact that chemical substances derived either from natural products or by synthetic means .. can give rise to toxicity in animals and human beings, and that they must be subjected to controls. The earliest writings speak of such toxicity and, from the times of ancient Egypt and in the Old Tes tament, controls have existed[l]. In the Middle Ages Paracelsus (1493-1541) noted that "All things are poisons, for there is nothing without poisonous qualities. It is only the dose which makes a thing a poison", and hence he stressed the importance of dose relative to toxic reactions [ 2] . Most of the early controls concentrated on substances that were to be deliberately administered to the human subject in the form of medicines. Legislation for many centuries was mainly con cerned with regulating the activities of apothecaries and physicians. The Royal College of Physicians, for example, was originally established to control the activities of physicians within London. Among the controls which it exerted was that over the use of medicinal substances. Such controls were, however, poor, based as they were on hearsay evidence of toxicity. For many centuries no means existed for the accurate determination of toxicity.

Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where organizations have reduced incidents by employing predictive analytics to foresee and mitigate future risks. It discusses how Predictive Safety Analytics is an opportunity to break through the plateau problem where safety rate improvements have stagnated in many organizations. The book presents how the use of data, coupled with advanced analytical techniques, including machine learning, has become a proven and successful innovation. Emphasis is placed on how the book can “meet you where you are” by illuminating a path to get there, starting with simple data the organization likely already has. Highlights of the book are the real examples and case studies that will assist in generating thoughts and ideas for what might work for individual readers and how they can adapt the information to their particular situations. This book is written for professionals and researchers in system reliability, risk and safety assessment, quality control, operational managers in selected industries, data scientists, and ML engineers. Students taking courses in these areas will also find this book of interest to them.

"Nearly all our safety data collection and reporting systems are backward-looking: incident reports; dashboards; compliance monitoring systems, and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where organizations have reduced incidents by employing predictive analytics to foresee and mitigate future risks. It discusses how Predictive Safety Analytics is an opportunity to break through the plateau problem where safety rate improvements have stagnated in many organizations. The book presents how the use of data, coupled with advanced analytical techniques, including machine learning, has become a proven and successful innovation. Emphasis is placed on how the book can "meet you where you are" by illuminating a path to get there, starting with simple data the organization likely already has. A highlight of the book is the real examples and case studies that will assist in generating thoughts and ideas for what might work for individual readers and how they can adapt the information to their particular situations. The book is written for professionals and researchers in system reliability, risk and safety assessment, quality control, operational managers in selected industries, data scientists, and ML engineers. Students taking courses in these areas will also find this book of interest"--

This edited text draws together the insights of numerous worldwide eminent academics to evaluate the condition of predictive policing and artificial intelligence (AI) as interlocked policy areas. Predictive and AI technologies are growing in prominence and at an unprecedented rate. Powerful digital crime mapping tools are being used to identify crime hotspots in real-time, as pattern-matching and search algorithms are sorting through huge police databases populated by growing volumes of data in an eff ort to identify people liable to experience (or commit) crime, places likely to host it, and variables associated with its solvability. Facial and vehicle recognition cameras are locating criminals as they move, while police services develop strategies informed by machine learning and other kinds of predictive analytics. Many of these innovations are features of modern policing in the UK, the US and Australia, among other jurisdictions. AI promises to reduce unnecessary labour, speed up various forms of police work, encourage police forces to more efficiently apportion their resources, and enable police officers to prevent crime and protect people from a variety of future harms. However, the promises of predictive and AI technologies and innovations do not always match reality. They often have significant weaknesses, come at a considerable cost and require challenging trade- off s to be made. Focusing on the UK, the US and Australia, this book explores themes of choice architecture, decision- making, human rights, accountability and the rule of law, as well as future uses of AI and predictive technologies in various policing contexts. The text contributes to ongoing debates on the benefits and biases of predictive algorithms, big data sets, machine learning systems, and broader policing strategies and challenges. Written in a clear and direct style, this book will appeal to students and scholars of policing, criminology, crime science, sociology, computer science, cognitive psychology and all those interested in the emergence of AI as a feature of contemporary policing.

Before now, biological systems could only be expressed in terms of linear relationships, however, as knowledge grows and new techniques of analysis on biological systems is made available, we are realizing the non-linearity of these systems. The concepts and techniques of nonlinear analysis allow for more realistic and accurate models in science. The Future of Pharmaceuticals: A Nonlinear Analysis provides an opportunity to understand the non-linearity of biological systems and its application in various areas of science, primarily pharmaceutical sciences. This book will benefit professionals in pharmaceutical industries, academia, and policy who are interested in an entirely new approach to how we will treat disease in the future. Key Features: Addresses a new approach of nonlinear analysis. Applies a theory of projection to chalk out the future, instead of basing on linear evolution. Provides an opportunity to better understand the non-linearity in biological systems and its applications in various areas of science, primarily pharmaceutical sciences. Helps change the thought process for those looking for answers to their questions which they do not find in the linear relationship approach. Encourages a broader perspective for the creative process of drug development.