Amphioxus Immunity: Tracing the Origin of Human Immunity covers a remarkable range of information about Amphioxus and its evolutionary context. This compilation of what is currently known about Amphioxus, with a sharp focus on its immune system, includes 13 topics, such as: Amphioxus as a model for understanding the evolution of vertebrates basic knowledge of immunology immune organs and cells of amphioxus a genomic and transcriptomic view of the Amphioxus immunity pattern recognition system in Amphioxus transcription factors in Amphioxus the complement system of Amphioxus the oxidative burst system in Amphioxus immune effectors in Amphioxus lipid signaling of immune response in Amphioxus apoptosis in amphioxus; primitive adaptive immune system of Amphioxus and future research directions This valuable reference book is loaded with information that will be useful for anyone who wishes to learn more about the origin of vertebrates and adaptive immunity.

The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.

The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also encompasses the preliminary knowledge of Machine Learning (ML) to VQA tasks and newly developed ML techniques for the purpose. Hence, firstly, it is particularly helpful to the beginner-readers (including research students) to enter into VQA field in general and LB-VQA one in particular. Secondly, new development in VQA and LB-VQA particularly are detailed in this book, which will give peer researchers and engineers new insights in VQA.