Atanu Bhattacharjee – författare

Alzheimer''s disease, one of the most rapidly growing neurodegenerative disorders, is characterized by a progressive loss of memory. Despite several advances in the field of medical therapeutics, a viable treatment for Alzheimer''s disease would be of great importance. Medicinal plants represent a largely untapped reservoir of natural medicines and potential sources of anti-Alzheimer’s drugs. The structural diversity of their phytoconstituents makes these plants a valuable source of novel lead compounds in the quest for drugs to treat Alzheimer''s disease. Based on traditional literature and up-to-date research, various new therapeutically active compounds have been identified from phytoextracts, which could be useful in the treatment of cognitive disorders.

Phytomedicine and Alzheimer’s Disease presents information on

This book serves as an excellent resource for scientific investigators, academics, biochemists, botanists, and alternative medicine practitioners who work to advance the role of phytomedicines in treating Alzheimer’s disease.

Alzheimer''s disease, one of the most rapidly growing neurodegenerative disorders, is characterized by a progressive loss of memory. Despite several advances in the field of medical therapeutics, a viable treatment for Alzheimer''s disease would be of great importance. Medicinal plants represent a largely untapped reservoir of natural medicines and potential sources of anti-Alzheimer’s drugs. The structural diversity of their phytoconstituents makes these plants a valuable source of novel lead compounds in the quest for drugs to treat Alzheimer''s disease. Based on traditional literature and up-to-date research, various new therapeutically active compounds have been identified from phytoextracts, which could be useful in the treatment of cognitive disorders.

Phytomedicine and Alzheimer’s Disease presents information on

This book serves as an excellent resource for scientific investigators, academics, biochemists, botanists, and alternative medicine practitioners who work to advance the role of phytomedicines in treating Alzheimer’s disease.

Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications of bayesian approach and wish to learn or enhance their skills in R and OpenBUGS, and those who are enrolled in R and OpenBUGS-based course for bayesian approach implementation. For those who have never used R/OpenBUGS, the book begins with a self-contained introduction to R that lays the foundation for later chapters.

Many books on the bayesian approach and the statistical analysis are advanced, and many are theoretical. While most of them do cover the objective, the fact remains that data analysis can not be performed without actually doing it, and this means using dedicated statistical software. There are several software packages, all with their specific objective. Finally, all packages are free to use, are versatile with problem-solving, and are interactive with R and OpenBUGS.

This book continues to cover a range of techniques related to oncology that grow in statistical analysis. It intended to make a single source of information on Bayesian statistical methodology for oncology research to cover several dimensions of statistical analysis. The book explains data analysis using real examples and includes all the R and OpenBUGS codes necessary to reproduce the analyses. The idea is to overall extending the Bayesian approach in oncology practice. It presents four sections to the statistical application framework:

This book is intended as a first course in bayesian biostatistics for oncology students. An oncologist can find useful guidance for implementing bayesian in research work. It serves as a practical guide and an excellent resource for learning the theory and practice of bayesian methods for the applied statistician, biostatistician, and data scientist.

Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications of bayesian approach and wish to learn or enhance their skills in R and OpenBUGS, and those who are enrolled in R and OpenBUGS-based course for bayesian approach implementation. For those who have never used R/OpenBUGS, the book begins with a self-contained introduction to R that lays the foundation for later chapters.

Many books on the bayesian approach and the statistical analysis are advanced, and many are theoretical. While most of them do cover the objective, the fact remains that data analysis can not be performed without actually doing it, and this means using dedicated statistical software. There are several software packages, all with their specific objective. Finally, all packages are free to use, are versatile with problem-solving, and are interactive with R and OpenBUGS.

This book continues to cover a range of techniques related to oncology that grow in statistical analysis. It intended to make a single source of information on Bayesian statistical methodology for oncology research to cover several dimensions of statistical analysis. The book explains data analysis using real examples and includes all the R and OpenBUGS codes necessary to reproduce the analyses. The idea is to overall extending the Bayesian approach in oncology practice. It presents four sections to the statistical application framework:

This book is intended as a first course in bayesian biostatistics for oncology students. An oncologist can find useful guidance for implementing bayesian in research work. It serves as a practical guide and an excellent resource for learning the theory and practice of bayesian methods for the applied statistician, biostatistician, and data scientist.

Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area.

Features:

This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.

Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area.

Features:

This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.

This reference book compiles the recent advances in computational and experimental modelling to screen and manage Alzheimer’s disease. It covers basic etiopathology and various in vitro and in vivo strategies of disease intervention. The book discusses how computer-aided drug design approaches reduce costs and increase biological test efficiency. It reviews the screening for anti-Alzheimer drugs and biomarker analysis of disease inhibitors. The book also explores mechanistic aspects of neurodegeneration and the use of natural products as therapeutics for Alzheimer’s disease.

Key features:

The book is meant for professionals, researchers, and students of neuroscience, psychology, and computational neurosciences.

This reference book compiles the recent advances in computational and experimental modelling to screen and manage Alzheimer’s disease. It covers basic etiopathology and various in vitro and in vivo strategies of disease intervention. The book discusses how computer-aided drug design approaches reduce costs and increase biological test efficiency. It reviews the screening for anti-Alzheimer drugs and biomarker analysis of disease inhibitors. The book also explores mechanistic aspects of neurodegeneration and the use of natural products as therapeutics for Alzheimer’s disease.

Key features:

The book is meant for professionals, researchers, and students of neuroscience, psychology, and computational neurosciences.

Delve into the realm of statistical methodology for mediation analysis with a Bayesian perspective in high dimensional data through this comprehensive guide. Focused on various forms of time-to-event data methodologies, this book helps readers master the application of Bayesian mediation analysis using R. Across ten chapters, this book explores concepts of mediation analysis, survival analysis, accelerated failure time modeling, longitudinal data analysis, and competing risk modeling. Each chapter progressively unravels intricate topics, from the foundations of Bayesian approaches to advanced techniques like variable selection, bivariate survival models, and Dirichlet process priors. With practical examples and step-by-step guidance, this book empowers readers to navigate the intricate landscape of high-dimensional data analysis, fostering a deep understanding of its applications and significance in diverse fields.

Delve into the realm of statistical methodology for mediation analysis with a Bayesian perspective in high dimensional data through this comprehensive guide. Focused on various forms of time-to-event data methodologies, this book helps readers master the application of Bayesian mediation analysis using R. Across ten chapters, this book explores concepts of mediation analysis, survival analysis, accelerated failure time modeling, longitudinal data analysis, and competing risk modeling. Each chapter progressively unravels intricate topics, from the foundations of Bayesian approaches to advanced techniques like variable selection, bivariate survival models, and Dirichlet process priors. With practical examples and step-by-step guidance, this book empowers readers to navigate the intricate landscape of high-dimensional data analysis, fostering a deep understanding of its applications and significance in diverse fields.

Phytoconstituents are a valuable source of new lead compounds due to their structural variety.

The accelerated rate of human ageing brought about by the 21st century''s longer life expectancy seems to be one of the major problems facing varied cultures worldwide. Ageing and the onset of chronic diseases appear to be significantly influenced by the type of food consumption pattern that people follow throughout their lives. There is strong evidence that Mediterranean diets are linked to a lower risk of brain disorders. A greater proportion of rural communities in the Mediterranean basin eat a diet high in plant-based foods daily. Stressors like reactive oxygen and reactive nitrogen species have a major negative influence on the brain in addition to the cardiovascular system.

Foods, particularly those derived from plants, are a great source of vitamins, antioxidants, polyphenols, and vital fatty acids, according to a growing body of epidemiological research. Studies indicate the effect of antioxidant vitamins on brain health, including ascorbic acid and α-tocopherol. Polyphenols and other plant-derived bioactivities influence how animal and human brains operate by reducing the amount of oxidative stress and controlling different signal transduction pathways, according to a large body of experimental evidence.

The present volume provides a collation of recent developments in the field with special reference to various types of plant-based phytoconstituents. The book''s content is relevant and interesting to a wide range of audiences from the backgrounds of plant sciences, pharmacy, biochemistry, and molecular biology. Apart from being a good reference material source, the book is relevant to plant biotechnologists and students/researchers in the field.

Phytoconstituents are a valuable source of new lead compounds due to their structural variety.

The accelerated rate of human ageing brought about by the 21st century''s longer life expectancy seems to be one of the major problems facing varied cultures worldwide. Ageing and the onset of chronic diseases appear to be significantly influenced by the type of food consumption pattern that people follow throughout their lives. There is strong evidence that Mediterranean diets are linked to a lower risk of brain disorders. A greater proportion of rural communities in the Mediterranean basin eat a diet high in plant-based foods daily. Stressors like reactive oxygen and reactive nitrogen species have a major negative influence on the brain in addition to the cardiovascular system.

Foods, particularly those derived from plants, are a great source of vitamins, antioxidants, polyphenols, and vital fatty acids, according to a growing body of epidemiological research. Studies indicate the effect of antioxidant vitamins on brain health, including ascorbic acid and α-tocopherol. Polyphenols and other plant-derived bioactivities influence how animal and human brains operate by reducing the amount of oxidative stress and controlling different signal transduction pathways, according to a large body of experimental evidence.

The present volume provides a collation of recent developments in the field with special reference to various types of plant-based phytoconstituents. The book''s content is relevant and interesting to a wide range of audiences from the backgrounds of plant sciences, pharmacy, biochemistry, and molecular biology. Apart from being a good reference material source, the book is relevant to plant biotechnologists and students/researchers in the field.

This book presents a practical approach for researchers seeking to analyse patient data over time. It serves as a comprehensive guide, utilising the R programming language to analyse complex datasets efficiently. It provides step-by-step instructions and examples, aiding in data organisation and insightful analysis to accurately predict event occurrences and the impact of different variables on patient outcomes, enhancing decision-making in medical practice.

It covers data preprocessing, integration, and advanced modelling techniques to serve as a valuable resource for professionals and researchers seeking evidence-based decision-making in healthcare and related fields.

This book presents a practical approach for researchers seeking to analyse patient data over time. It serves as a comprehensive guide, utilising the R programming language to analyse complex datasets efficiently. It provides step-by-step instructions and examples, aiding in data organisation and insightful analysis to accurately predict event occurrences and the impact of different variables on patient outcomes, enhancing decision-making in medical practice.

It covers data preprocessing, integration, and advanced modelling techniques to serve as a valuable resource for professionals and researchers seeking evidence-based decision-making in healthcare and related fields.