Human iPSC-derived Disease Models for Drug Discovery (1版)

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Since their development a decade ago, human induced pluripotent stem cells (iPSC) have revolutionized the study of human disease, given rise to regenerative medicine technologies, and provided exceptional opportunities for pharmacologic research. These cells provide an essentially unlimited supply of cell types that are difficult to obtain from patients, such as neurons or cardiomyocytes, or are difficult to maintain in primary cell culture. iPSC can be obtained from patients afflicted with a particular disease but, in combination with recently developed gene editing techniques, can also be modified to generate disease models. Moreover, the new techniques of 3 Dimensional printing and materials science facilitate the generation of organoids that can mirror organs under disease conditions. These properties make iPSC powerful tools to study how diseases develop and how they may be treated. In addition, iPSC can also be used to treat conditions in which the target cell population has beenlost and such regenerative approaches hold great promise for currently untreatable diseases, including cardiac failure or photoreceptor degenerations.

Stochastic Models for Prices Dynamics in Energy and Commodity Markets: An Infinite-Dimensional Perspective

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This monograph presents a theory for random field models in time and space, viewed as stochastic processes with values in a Hilbert space, to model the stochastic dynamics of forward and futures prices in energy, power, and commodity markets. In this book, the well-known Heath–Jarrow–Morton approach from interest rate theory is adopted and extended into an infinite-dimensional framework, allowing for flexible modeling of price stochasticity across time and along the term structure curve. Various models are introduced based on stochastic partial differential equations with infinite-dimensional Lévy processes as noise drivers, emphasizing random fields described by low-dimensional parametric covariance functions instead of classical high-dimensional factor models. The Filipović space, a separable Hilbert space of Sobolev type, is found to be a convenient state space for the dynamics of forward and futures term structures. The monograph provides a classification of important operators in this space, covering covariance operators and the stochastic modeling of volatility term structures, including the Samuelson effect. Fourier methods are employed to price many derivatives of interest in energy, power, and commodity markets, and sensitivity 'delta' expressions can be derived. Additionally, the monograph covers forward curve smoothing, the connection between forwards with fixed delivery and delivery period, as well as the classical theory of forward and futures pricing. This monograph will appeal to researchers and graduate students interested in mathematical finance and stochastic analysis applied in the challenging markets of energy, power, and commodities. Practitioners seeking sophisticated yet flexible and analytically tractable risk models will also find it valuable.

Network Models for Data Science: Theory, Algorithms, and Applications

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This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component.