A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobilebased platforms). Some significant new topics are identified and listed as new directions. By providing a stateof-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.

a b s t r a c t 26 Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new 27 but similar problems much more quickly and effectively. In contrast to classical machine learning 28 methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains 29 to facilitate predictive modeling consisting of different data patterns in the current domain. To improve 30 the performance of existing transfer learning methods and handle the knowledge transfer process in 31 real-world systems, computational intelligence has recently been applied in transfer learning. This paper 32 systematically examines computational intelligence-based transfer learning techniques and clusters 33 related technique developments into four main categories: (a) neural network-based transfer learning; 34 (b) Bayes-based transfer learning; (c) fuzzy transfer learning, and (d) applications of computational 35 intelligence-based transfer learning. By providing state-of-the-art knowledge, this survey will directly 36 support researchers and practice-based professionals to understand the developments in computational 37 intelligence-based transfer learning research and applications.38

The phenomenon of multidrug resistance (MDR) in cancer is associated with the overexpression of the ATP-binding cassette (ABC) transporter proteins, including multidrug resistance-associated protein 1 (MRP1) and P-glycoprotein. MRP1 plays an active role in protecting cells by its ability to efflux a vast array of drugs to sub-lethal levels. There has been much effort in elucidating the mechanisms of action, structure and substrates and substrate binding sites of MRP1 in the last decade. In this review, we detail our current understanding of MRP1, its clinical relevance and highlight the current environment in the search for MRP1 inhibitors. We also look at the capacity for the rapid intercellular transfer of MRP1 phenotype from spontaneously shed membrane vesicles known as microparticles and discuss the clinical and therapeutic significance of this in the context of cancer MDR.