Proximity As Method

This book examines proximity as a benchmarked concept that can be deployed across a range of humanities disciplines to rethink the ways in which existences in the world are always already coexistences – and to parse the heuristic, ethical, epistemological, praxeological consequences of this recognition. The volume: - Brings together diverse theoretical approaches and utilizes a range of methodological instruments – conceptual, textual-analytic (whether in the realm of literary or religious studies, or theology or law), archival, digital, sociological or politological; - Includes empirical case-studies that allow calibrated and scaled exemplifications; - Launches forays onto unexplored conceptual terrain, or call into question hallowed truths of scholarly procedure. The volume will be essential reading for students and early researchers in the social sciences and the humanities.

This book provides detailed protocols and untold tips and tricks regarding the most well-known examples of proximity labeling methods, in which the protein of interest is genetically fused to or labeled with an enzyme that can generate short-lived reactive species to non-specifically label molecules within a certain radius of up to twenty nanometers. Beginning with peroxidase-based proximity labeling methods, the volume continues with BioID, proximity labeling methods that describe the proximity ligation assay to detect RNA-DNA interactions, UV cross-linking to demonstrate RNA-protein interactions, and how chemical and enzymatic reactivities can be improved upon DNA-DNA and protein-protein interactions, as well as “proximity-induced self-labeling,” where the radius of labeling is zero. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Proximity Labeling: Methods and Protocols serves as an ideal guide for researchers exploring the crucial roles that proximity-driven reactions play in biological systems.

This two-volume set LNCS 7902 and 7903 constitutes the refereed proceedings of the 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, held in Puerto de la Cruz, Tenerife, Spain, in June 2013. The 116 revised papers were carefully reviewed and selected from numerous submissions for presentation in two volumes. The papers explore sections on mathematical and theoretical methods in computational intelligence, neurocomputational formulations, learning and adaptation emulation of cognitive functions, bio-inspired systems and neuro-engineering, advanced topics in computational intelligence and applications.

This book examines proximity as a benchmarked concept that can be deployed across a range of humanities disciplines to rethink the ways in which existences in the world are always already coexistences - and to parse the heuristic, ethical, epistemological, praxeological consequences of this recognition. The volume: - Brings together diverse theoretical approaches and utilizes a range of methodological instruments - conceptual, textual-analytic (whether in the realm of literary or religious studies, or theology or law), archival, digital, sociological or politological; - Includes empirical case-studies that allow calibrated and scaled exemplifications; - Launches forays onto unexplored conceptual terrain, or call into question hallowed truths of scholarly procedure. The volume will be essential reading for students and early researchers in the social sciences and the humanities.

Sensors were developed to detect and quantify structures and functions of human body as well as to gather information from the environment in order to optimize the efficiency, cost-effectiveness and quality of healthcare services as well as to improve health and quality of life. This book offers an up-to-date overview of the concepts, modeling, technical and technological details and practical applications of different types of sensors. It also discusses the trends for the next generation of sensors and systems for healthcare settings. It is aimed at researchers and graduate students in the field of healthcare technologies, as well as academics and industry professionals involved in developing sensing systems for human body structures and functions, and for monitoring activities and health.

Learn Big Data from the ground up with this complete and up-to-date resource from leaders in the field Big Data: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data. You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work. Big Data also covers the central topic of big data visualization with Tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software. Accessibly organized, Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include: The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization Perfect for data scientists, data engineers, and database managers, Big Data also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book.

The notion of proximity is increasing in popularity in economic and geographic literature, and is now commonly used by scholars in regional science and spatial economics.

Designed for reviewers of research manuscripts and proposals in the social and behavioral sciences, and beyond, this title includes chapters that address traditional and emerging quantitative methods of data analysis.