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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.
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