In this issue of PET Clinics, guest editors Drs. Harshad R. Kulkarni and Abass Alavi bring their considerable expertise to the topic of Prostate Cancer. PET imaging for prostate cancer continues to evolve as new radiotracers and imaging modalities are combined. This issue offers an up-to-date review of the most popular radiotracers and how PET imaging is combined with MR, CT, and ultrasound to provide the most accurate diagnosis of prostate cancer. Contains 12 practice-oriented topics including the role of ultrasound, CT, and MRI in managing patients with prostate cancer; Ga68 PSMA imaging; PET imaging for prostate cancer using F-18 Fluciclovine; PET imaging for prostate cancer using Ga-68 RM2; the role of NaF PET in the imaging of prostate cancer; and more. Provides in-depth clinical reviews on prostate cancer, offering actionable insights for clinical practice. Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.

This collection features five peer-reviewed reviews on managing bacterial diseases of poultry. The first chapter provides a brief overview of Campylobacter in poultry production and the routes of transmission from bird to human. It considers current and future challenges in controlling Campylobacter, as well as the control measures implemented to reduce the occurrence of infection in poultry flocks. The second chapter discusses economically-important enteric diseases and disorders of poultry. The chapter highlights the aetiology of these diseases and explores more sustainable alternatives to promote gastrointestinal health, such as plant-derived extracts, organic acids, prebiotics and probiotics. The third chapter provides an overview of best practices to manage disease outbreaks in poultry. It reviews current disease preventative measures, health monitoring procedures, as well as disease investigation techniques. The fourth chapter reviews the emergence and re-emergence of several infectious pathogens in poultry production and the consequent need to improve current biosecurity measures in poultry flocks to ensure optimal animal health and welfare. The final chapter considers the main alternatives to antibiotics in preventing zoonoses and other major pathogens in poultry. The chapter reviews the sector's use of prebiotics and related compounds, as well as the beneficial effects of their use.

This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn Implement various techniques in time series analysis using Python. Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecasting Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.