Develop Effective Immunogenicity Risk Mitigation Strategies Immunogenicity assessment is a prerequisite for the successful development of biopharmaceuticals, including safety and efficacy evaluation. Using advanced statistical methods in the study design and analysis stages is therefore essential to immunogenicity risk assessment and mitigation strategies. Statistical Methods for Immunogenicity Assessment provides a single source of information on statistical concepts, principles, methods, and strategies for detection, quantification, assessment, and control of immunogenicity. The book first gives an overview of the impact of immunogenicity on biopharmaceutical development, regulatory requirements, and statistical methods and strategies used for immunogenicity detection, quantification, and risk assessment and mitigation. It then covers anti-drug antibody (ADA) assay development, optimization, validation, and transfer as well as the analysis of cut point, a key assay performance parameter in ADA assay development and validation. The authors illustrate how to apply statistical modeling approaches to establish associations between ADA and clinical outcomes, predict immunogenicity risk, and develop risk mitigation strategies. They also present various strategies for immunogenicity risk control. The book concludes with an explanation of the computer codes and algorithms of the statistical methods. A critical issue in the development of biologics, immunogenicity can cause early termination or limited use of the products if not managed well. This book shows how to use robust statistical methods for detecting, quantifying, assessing, and mitigating immunogenicity risk. It is an invaluable resource for anyone involved in immunogenicity risk assessment and control in both non-clinical and clinical biopharmaceutical development.

“While he’s out for a walk in the autumn woods, Mr. Cat discovers a wee, thumb-sized girl who brings love, light, and laughter into his regimented days. The little sprite spreads joy and inspires creativity, brightening the cold winter months. Her bare feet and sunny yellow dress stand in contrast to Mr. Cat’s formal manners. This is a bittersweet tale of hope; it celebrates the beauty and transience of life and the passing seasons.� – Foreword Reviews "A story with quite engaging illustrations.� – Kirkus Reviews "The careful, softly shaded illustrations are gently luminous.� – Publishers Weekly Mr. Cat spends his days alone. When he goes out to paint a beautiful winter landscape, he finds a little girl. He decides to take her home. The girl colors his world… until she disappears A poetic story about friendship and saying good-bye. For daydreamers ages 5 years and up. Guided Reading Level O

This book highlights the latest advances in the use of graphene and bio-compatible-material-decorated graphene to detect various targets (e.g. DNA, RNA, amino acids, peptides, proteins, enzymes, antigens, glucose, DA, AA, UA, ATP, NADH, gas, ions, etc.). It focuses on the specific interaction of these substances with graphene (or modified graphene) and the efficient transduction of the target recognition event into detectable signals via various techniques. Particular emphasis is given to well-designed strategies for constructing graphene-based platforms and target determination. It also covers other bio-analytical applications including cellular imaging, drug delivery and bacteria inhibition, before turning to a discussion of future challenges and prospects of graphene in bio-analytical applications. This book is intended for researchers working in the fields of analytical chemistry, nanomaterials and biomedical engineering. Li Niu is a Professor at the State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences.

The 105 theses contained in this book are selected from those whose authors were present at the 20th International Symposium on Acoustical Imaging. held at Southeast University. Nanjing. China. during September 12-14. 1992. It was the first time that the symposium had been held in China. Our efforts to host the conference goes back to the 15th International Symposium on Acoustical Imaging held in Halifax. Canada. in 1986. We are glad that the 20th symposium has been successfully held at last. We are ardent for the symposium not only because we attach much importance to the field of acoustical imaging. but also because we admire the tradition of the serious academic exploration and friendly cooperation of the scholars attending the symposium. The theses in this book are from 21 countries and those by Mr. G. Wade. Takuso Sato. J. F. Greenleaf. K. J. Langenberg. and Wencai Yang are the specially invited papers. These theses cover such important fields of acoustical imaging as follows: 1. Mathematics and physics of acoustical imaging; 2. Components and industry application; 3. Applications in medicine and biology; 4. Applications in nondestructive testing; 5. Applications in geophysics; 6. Underwater acoustical imaging. All these theses reflect the latest progress in theory and technology. We are very grateful to all the authors who have provided these theses.

"While he's out for a walk in the autumn woods, Mr. Cat discovers a wee, thumb-sized girl who brings love, light, and laughter into his regimented days. The little sprite spreads joy and inspires creativity, brightening the cold winter months. Her bare feet and sunny yellow dress stand in contrast to Mr. Cat's formal manners. This is a bittersweet tale of hope; it celebrates the beauty and transience of life and the passing seasons." - Foreword Reviews "A story with quite engaging illustrations." - Kirkus Reviews "The careful, softly shaded illustrations are gently luminous." - Publishers Weekly Mr. Cat spends his days alone. When he goes out to paint a beautiful winter landscape, he finds a little girl. He decides to take her home. The girl colors his world... until she disappears A poetic story about friendship and saying good-bye. For daydreamers ages 5 years and up. Guided Reading Level O

Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code About This Book * Apply R to simplify predictive modeling with short and simple code * Use machine learning to solve problems ranging from small to big data * Build a training and testing dataset, applying different classification methods. Who This Book Is For This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful. What You Will Learn * Create and inspect transaction datasets and perform association analysis with the Apriori algorithm * Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm * Compare differences between each regression method to discover how they solve problems * Detect and impute missing values in air quality data * Predict possible churn users with the classification approach * Plot the autocorrelation function with time series analysis * Use the Cox proportional hazards model for survival analysis * Implement the clustering method to segment customer data * Compress images with the dimension reduction method * Incorporate R and Hadoop to solve machine learning problems on big data In Detail Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier. Style and approach This is an easy-to-follow guide packed with hands-on examples of machine learning tasks. Each topic includes step-by-step instructions on tackling difficulties faced when applying R to machine learning.

Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning About This Book * Proficiently analyze data and apply machine learning techniques * Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R * Construct a predictive model by using a variety of machine learning packages Who This Book Is For This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R - it will help you increase your R expertise, regardless of your level of experience. What You Will Learn * Get data into your R environment and prepare it for analysis * Perform exploratory data analyses and generate meaningful visualizations of the data * Generate various plots in R using the basic R plotting techniques * Create presentations and learn the basics of creating apps in R for your audience * Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm * Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm * Build, tune, and evaluate predictive models with different machine learning packages * Incorporate R and Hadoop to solve machine learning problems on big data In Detail The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis - this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data - this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning - this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: * R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan * R Data Visualization Cookbook by Atmajitsinh Gohil * Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu) Style and approach This course creates a smooth learning path that will teach you how to analyze data and create stunning visualizations. The step-by-step instructions provided for each recipe in this comprehensive Learning Path will show you how to create machine learning projects with R.

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques About This Book * Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages * Understand how to apply useful data analysis techniques in R for real-world applications * An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis Who This Book Is For This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages. What You Will Learn * Get to know the functional characteristics of R language * Extract, transform, and load data from heterogeneous sources * Understand how easily R can confront probability and statistics problems * Get simple R instructions to quickly organize and manipulate large datasets * Create professional data visualizations and interactive reports * Predict user purchase behavior by adopting a classification approach * Implement data mining techniques to discover items that are frequently purchased together * Group similar text documents by using various clustering methods In Detail This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the "dplyr" and "data.table" packages to efficiently process larger data structures. We also focus on "ggplot2" and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the "ggvis" package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis. Style and approach This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.

If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.

Among the many existing categories of face de- tection algorithms, the sample-based method is one of the most widely-used approaches. The essence of the sample-based method is to solve a two-class classification problem of face versus non-face. Many classification algorithms such as the Naive Bayesian, Neural Network and Support Vector Machines (SVM) have been used for this purpose. This thesis showcases a research study into face detection technologies. It has two main parts. Firstly, in the sample preparation section, new passive sample selection and active sample generation algorithms are proposed to assist existing sample-based algorithms in solving the problem of face detection. Secondly, in the classification section, a new Bayesian-based classification method is proposed for face detection.

Mathematics and Physics 1: New Ways to Use Old Ideas for High Quality Acoustic Images; G. Wade. Mathematics and Physics 2: A Modified Algorithm for Ultrasound Holographic Q Imaging; Q. Zhengdi. Application in Nondestructive Testing: Imaging and Inverse Scattering in Nondestructive Evaluation; K.J. Langenberg, et al. Acoustical and Optoacoustical Microscope: Nearfield Scanning Acoustic Microscopy; A. Kulik, et al. Transducers and Array: Formation and Propagation of Limited Diffraction Beams; J. Lu, J.F. Greenleaf. Application in Medicine and Biology 1: Multidimensional Cardiac Imaging; J.F. Greenleaf, et al. Application in Medicine and Biology 2: A Restoration Method of Ultrasonic Medical Tomogram; K. Zhou, et al. Seismic Imaging: A Method of Diffraction Tomography; K. Nagai, et al. Underwater Acoustical Imaging: A New Generation Side Scan Sonar; P. Alais, et al. 96 additional articles. Index.