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The complex interactions between the innate and adaptive immune systems function to recognize and clear pathogens or transformed cells, but inefficient interactions between these two systems can result in harmful immunologic responses including chronic infections and the development of cancer. Several hallmarks of dysfunctional adaptive immune responses often detected in tumors share specific features with ineffective immunity in chronic infections. The members of the micromilieu actively participate in the process of tumorigenesis or chronification of infection by modulating innate and adaptive immune system interactions leading e.g. to insufficient T cell responses. The best example is given by the acquisition of an “exhausted” state of cytotoxic CD8+ T cells (CTLs) responding to chronic infections or tumors that are associated with elevated expression of inhibitory receptors and impaired cytokine response. Targeting these major inhibitory pathways by immune checkpoint blockers represents a prime example of successful clinical translation of tumor-specific immunotherapies. Understanding the mechanisms behind (mal)adaptations of the immune system is crucial for achieving therapeutic benefits. The establishment and co-evolution of a dynamic microenvironment niche constituted by the recruitment of numerous cell types dampen immune responses and thus contribute to the development of neoplastic transformation as well as infection. Although there are examples of successful immunotherapeutic approaches (CAR-T cells, immune checkpoint inhibitors, or mRNA vaccination), a large percentage of patients with cancer or chronic infections still do not benefit from these therapies or develop severe immune-related adverse events. The reasons for these failures are not well understood. A possible explanation might be that current immunotherapies target predominantly the effector arm of the immune system by trying to reactivate dysfunctional T cells, but do not sufficiently address the influence of the innate immune system and the contributions of the tumor microenvironment (TME) niche. The main problem we would like to address in this special issue is how inappropriate function of the innate immune system affects adaptive immunity and contributes to inefficient anti-cancer immunity and chronification of infections. The central goal is to provide a more precise understanding of the various (common and novel) immune evasion mechanisms in cancers and in chronic infections to obtain a detailed map of common and disease-specific immune escape checkpoints. To that aim, we want to compile a wide array of interdisciplinary studies exploring a comparative and multi-layered analysis of mechanisms responsible for inefficient immune responses, including novel approaches i.e. multi-omics or epigenetic signaling. We would also like to combine studies from different fields, including basic and clinical immunology, oncology, and virology/microbiology. We welcome the submission of Original Research, Review, Mini-Review, Methods, Case report, and Perspective articles that cover, but are not limited to the following topics: • Convergent mechanisms supporting immune escape in preclinical models (tumors and chronic infections) • Convergent evasion mechanisms mediated by tumor-infiltrating suppressive cells (Treg, MDSC, macro-phages, soluble mediators, signaling, metabolism, ...) • Convergent immune evasion mechanisms mediated by chronic infection (viral or parasite) • Novel strategies to modulate the TME by direct or indirect targeting of immune suppressor cells. • Approaches to enhance persistence and resilience of anticancer T cells • Combinatorial therapeutic strategies (mRNA, antibodies, immune checkpoint blockers …) that target convergent immune evasion mechanisms Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this topic.
"The Book and the Sword was Louis Cha's first novel, published in 1955. The story has a panoramic sweep which has at its heart a few unbeatable themes: secret societies, kung fu masters, and the sensational rumour so dear to Chinese hearts that the great Manchu Emperor Qian Long was not in fact a Manchu but a Han Chinese, a line of descent that came about as a result of a 'baby swap' on the part of the Chens of Haining in Southern China. It mixes in the exotic flavours of central Asia, a lost city in the desert guarded by wolf packs, and the Fragrant Princess. This lady is an embellishment of an actual historical figure - although whether she actually smelled of flowers, we will never know."--Jacket
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
The aim of this Research Topic is to discuss the state of the art on the use of Information-based methods in the analysis of neuroimaging data. Information-based methods, typically built as extensions of the Shannon Entropy, are at the basis of model-free approaches which, being based on probability distributions rather than on specific expectations, can account for all possible non-linearities present in the data in a model-independent fashion. Mutual Information-like methods can also be applied on interacting dynamical variables described by time-series, thus addressing the uncertainty reduction (or information) in one variable by conditioning on another set of variables. In the last years, different Information-based methods have been shown to be flexible and powerful tools to analyze neuroimaging data, with a wide range of different methodologies, including formulations-based on bivariate vs multivariate representations, frequency vs time domains, etc. Apart from methodological issues, the information bit as a common unit represents a convenient way to open the road for comparison and integration between different measurements of neuroimaging data in three complementary contexts: Structural Connectivity, Dynamical (Functional and Effective) Connectivity, and Modelling of brain activity. Applications are ubiquitous, starting from resting state in healthy subjects to modulations of consciousness and other aspects of pathophysiology. Mutual Information-based methods have provided new insights about common-principles in brain organization, showing the existence of an active default network when the brain is at rest. It is not clear, however, how this default network is generated, the different modules are intra-interacting, or disappearing in the presence of stimulation. Some of these open-questions at the functional level might find their mechanisms on their structural correlates. A key question is the link between structure and function and the use of structural priors for the understanding of the functional connectivity measures. As effective connectivity is concerned, recently a common framework has been proposed for Transfer Entropy and Granger Causality, a well-established methodology originally based on autoregressive models. This framework can open the way to new theories and applications. This Research Topic brings together contributions from researchers from different backgrounds which are either developing new approaches, or applying existing methodologies to new data, and we hope it will set the basis for discussing the development and validation of new Information-based methodologies for the understanding of brain structure, function, and dynamics.
The Norbert Wiener Center for Harmonic Analysis and Applications provides a state-of-the-art research venue for the broad emerging area of mathematical engineering in the context of harmonic analysis. This two-volume set consists of contributions from speakers at the February Fourier Talks (FFT) from 2006-2011. The FFT are organized by the Norbert Wiener Center in the Department of Mathematics at the University of Maryland, College Park. These volumes span a large spectrum of harmonic analysis and its applications. They are divided into the following parts: Volume I · Sampling Theory · Remote Sensing · Mathematics of Data Processing · Applications of Data Processing Volume II · Measure Theory · Filtering · Operator Theory · Biomathematics Each part provides state-of-the-art results, with contributions from an impressive array of mathematicians, engineers, and scientists in academia, industry, and government. Excursions in Harmonic Analysis: The February Fourier Talks at the Norbert Wiener Center is an excellent reference for graduate students, researchers, and professionals in pure and applied mathematics, engineering, and physics.
The second novel by the phenomenally talented Alice Oseman, the author of the million-copy bestselling Heartstopper books—now a major Netflix series. What if everything you set yourself up to be was wrong? Frances has always been a study machine with one goal: elite university. Nothing will stand in her way. Not friends, not a guilty secret—not even the person she is on the inside. But when Frances meets Aled, the shy genius behind her favorite podcast, she discovers a new freedom. He unlocks the door to Real Frances and for the first time she experiences true friendship, unafraid to be herself. Then the podcast goes viral and the fragile trust between them is broken. Caught between who she was and who she longs to be, Frances’s dreams come crashing down. Suffocating with guilt, she knows that she has to confront her past… She has to confess why Carys disappeared… Meanwhile at university, Aled is alone, fighting even darker secrets. It’s only by facing up to your fears that you can overcome them. And it’s only by being your true self that you can find happiness. Frances is going to need every bit of courage she has. A coming-of-age read that tackles issues of identity, the pressure to succeed, diversity, and freedom to choose, Radio Silence is a tour de force by the most exciting writer of her generation.