The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 47th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, constitutes a special issue focusing on Digital Ecosystems and Social Networks. The 9 revised selected papers cover topics that include Social Big Data, Data Analysis, Cloud-Based Feedback, Experience Ecosystems, Pervasive Environments, and Smart Systems.

In the rapidly evolving world of technology, understanding foundational concepts like data structures, specifically lists, and their manipulation is essential. This book aims to delve deep into the practicalities of using lists in Python, a versatile and widely-used programming language known for its ease of use and powerful libraries. Coupled with this, the book explores the graphical user interface library, Tkinter, providing a comprehensive guide on how to make Python's capabilities more interactive and user-friendly. The significance of lists in programming cannot be overstated. They are among the most basic and crucial data structures in computer science, essential for storing sequences of data that are dynamically modifiable. In Python, lists are used extensively across simple applications to high-end data processing tasks. This book will start by exploring the anatomy of lists in Python, covering their creation, manipulation, and application in various real-world scenarios. Following the understanding of lists, the discussion will transition to operations on lists. Operations like appending, slicing, sorting, and more are pivotal in handling data efficiently. Through practical examples and detailed explanation, readers will learn how these operations are implemented in Python and how they can be used to solve common programming problems. Moreover, the power of list comprehensions, a distinctive feature of Python that allows for concise and efficient manipulation of lists, will be thoroughly discussed. This feature not only simplifies code but also enhances its readability and efficiency, making Python an appealing choice for developers. However, theoretical knowledge of these operations and their syntax only scratches the surface of their potential. To bridge the gap between theory and practical application, this book incorporates interactive examples using Tkinter, Python’s standard GUI library. Tkinter allows programmers to create graphical interfaces, making software applications accessible to a broader audience, including those who might not be comfortable with command-line interfaces. Integrating list operations into a GUI can significantly enhance the functionality and user-friendliness of applications. For instance, users can interact with the data more intuitively, perform operations in real-time, and see the results immediately, which is crucial for learning and debugging. The chapters dedicated to Tkinter will guide readers through setting up their first GUI applications. Starting from basic windows and widgets, the discussion will evolve to include how list operations can be integrated into these interfaces. Whether it's displaying a list, updating it based on user input, or sorting and filtering data based on user commands, the book will cover a wide range of use cases. One of the core strengths of combining list operations with Tkinter is in educational software, where interactive tools can significantly enhance the learning experience. By allowing students to manipulate data structures in real-time, they can see the immediate impact of their actions, thereby deepening their understanding of the subject matter. Furthermore, this approach has applications in professional software development, where developers need to build applications that are not only functional but also intuitive and responsive. The book will explore several project ideas and real-world applications, showing how the concepts discussed can be used to build meaningful and efficient software. Beyond educational and professional environments, this integration finds relevance in data analysis and visualization tasks. Analysts often need to manipulate large datasets and visualize their results effectively. Here, Python’s list operations and Tkinter’s graphical capabilities come together to offer powerful tools for data manipulation and display. In addition to practical applications, the book also addresses best practices and common pitfalls in both list manipulation and GUI development. Understanding these will help readers avoid common errors and improve the performance of their code. As technology continues to advance, the importance of understanding foundational programming skills and integrating them into user-friendly applications cannot be overstated. This book is designed not just to teach but also to inspire its readers to explore the possibilities of Python and Tkinter, encouraging them to develop applications that are powerful, efficient, and user-centric. In conclusion, this book serves as a comprehensive guide for anyone looking to deepen their understanding of Python’s list operations and GUI development using Tkinter. By the end of this book, readers will not only be proficient in these areas but will also be equipped to apply these skills in practical, innovative, and effective ways..

This title showcases modern environmental modelling methods, the basic theory behind them and their incorporation into complex environmental investigations.

This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics.

This book is unique in covering a wide range of design and analysis issues in genetic studies of rare variants, taking advantage of collaboration of the editors with many experts in the field through large-scale international consortia including the UK10K Project, GO-T2D and T2D-GENES. Chapters provide details of state-of-the-art methodology for rare variant detection and calling, imputation and analysis in samples of unrelated individuals and families. The book also covers analytical issues associated with the study of rare variants, such as the impact of fine-scale population structure, and with combining information on rare variants across studies in a meta-analysis framework. Genetic association studies have in the last few years substantially enhanced our understanding of factors underlying traits of high medical importance, such as body mass index, lipid levels, blood pressure and many others. There is growing empirical evidence that low-frequency and rare variants play an important role in complex human phenotypes. This book covers multiple aspects of study design, analysis and interpretation for complex trait studies focusing on rare sequence variation. In many areas of genomic research, including complex trait association studies, technology is in danger of outstripping our capacity to analyse and interpret the vast amounts of data generated. The field of statistical genetics in the whole-genome sequencing era is still in its infancy, but powerful methods to analyse the aggregation of low-frequency and rare variants are now starting to emerge. The chapter Functional Annotation of Rare Genetic Variants is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Statistical Bioinformatics provides a balanced treatment of statistical theory in the context of bioinformatics applications. Designed for a one or two semester senior undergraduate or graduate bioinformatics course, the text takes a broad view of the subject – not just gene expression and sequence analysis, but a careful balance of statistical theory in the context of bioinformatics applications. The inclusion of R & SAS code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the important foundation needed to conduct bioinformatics. - Integrates biological, statistical and computational concepts - Inclusion of R & SAS code - Provides coverage of complex statistical methods in context with applications in bioinformatics - Exercises and examples aid teaching and learning presented at the right level - Bayesian methods and the modern multiple testing principles in one convenient book

Ronald Fisher needed to develop elaborate models of genetic effects in order to set the foundations of Quantitative Genetics in his 1918 paper “The correlation between relatives on the supposition of Mendelian inheritance”. Since then, many significant implementations have been made to model genetic effects. However, at the verge of one century after Fisher’s kick-off, models of genetic effects keep on being discussed and implemented. Indeed, the relatively recent advent of QTL analyses challenged the state of the art of this field by providing researchers the opportunity to obtain and analyze estimates of genetic effects from real data. In this context, the development of this field was not exempt of some polemics, like the debate about the convenience of the functional and the statistical epistasis approaches. This research topic is meant to provide recent developments in models and estimation of genetic effects and to enrich the discussion about how and why models of genetic effects must be further developed and applied. The articles in this Research Topic shall thus extend, refine and/or provide a refresh look at Fisher’s original models of genetic effects and their application to genetic effects estimation and to improve our understanding of evolutionary processes and breeding programs.