Abstract. How can micro-blogging activities on Twitter be leveraged for user modeling and personalization? In this paper we investigate this question and introduce a framework for user modeling on Twitter which enriches the semantics of Twitter messages (tweets) and identifies topics and entities (e.g. persons, events, products) mentioned in tweets. We analyze how strategies for constructing hashtag-based, entity-based or topic-based user profiles benefit from semantic enrichment and explore the temporal dynamics of those profiles. We further measure and compare the performance of the user modeling strategies in context of a personalized news recommendation system. Our results reveal how semantic enrichment enhances the variety and quality of the generated user profiles. Further, we see how the different user modeling strategies impact personalization and discover that the consideration of temporal profile patterns can improve recommendation quality.

Abstract. As the most popular microblogging platform, the vast amount of content on Twitter is constantly growing so that the retrieval of relevant information (streams) is becoming more and more difficult every day. Representing the semantics of individual Twitter activities and modeling the interests of Twitter users would allow for personalization and therewith countervail the information overload. Given the variety and recency of topics people discuss on Twitter, semantic user profiles generated from Twitter posts moreover promise to be beneficial for other applications on the Social Web as well. However, automatically inferring the semantic meaning of Twitter posts is a non-trivial problem. In this paper we investigate semantic user modeling based on Twitter posts. We introduce and analyze methods for linking Twitter posts with related news articles in order to contextualize Twitter activities. We then propose and compare strategies that exploit the semantics extracted from both tweets and related news articles to represent individual Twitter activities in a semantically meaningful way. A large-scale evaluation validates the benefits of our approach and shows that our methods relate tweets to news articles with high precision and coverage, enrich the semantics of tweets clearly and have strong impact on the construction of semantic user profiles for the Social Web.

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Upconversion nanocrystals (UCNs) display near-infrared (NIR)-responsive photoluminescent properties for NIR imaging and drug delivery. The development of effective strategies for UCN integration with other complementary nanostructures for targeting and drug conjugation is highly desirable. This study reports on a core/shell-based theranostic system designed by UCN integration with a folate (FA)-conjugated dendrimer for tumor targeting and with photocaged doxorubicin as a cytotoxic agent. Two types of UCNs (NaYF4:Yb/Er (or Yb/Tm); diameter = ≈50 to 54 nm) are described, each displaying distinct emission properties upon NIR (980 nm) excitation. The UCNs are surface modified through covalent attachment of photocaged doxorubicin (ONB-Dox) and a multivalent FA-conjugated polyamidoamine (PAMAM) dendrimer G5(FA)6 to prepare UCN@(ONB-Dox)(G5FA). Surface plasmon resonance experiments performed with G5(FA)6 dendrimer alone show nanomolar binding avidity (KD = 5.9 × 10(-9) M) to the folate binding protein. This dendrimer binding corresponds with selective binding and uptake of UCN@(ONB-Dox)(G5FA) by FAR-positive KB carcinoma cells in vitro. Furthermore, UCN@(ONB-Dox)(G5FA) treatment of FAR(+) KB cells inhibits cell growth in a light dependent manner. These results validate the utility of modularly integrated UCN-dendrimer nanocomposites for cell type specific NIR imaging and light-controlled drug release, thus serving as a new theranostic system.

Social Web describes a new culture of participation on the Web where more and more people actively participate in publishing and organizing Web content. As part of this culture, people leave a variety of traces when interacting with (other people via) Social Web systems. In this paper, we investigate user modeling strategies for inferring personal interest profiles from Social Web interactions. In particular, we analyze individual micro-blogging activities on Twitter. We compare different strategies for creating user profiles based on the Twitter messages a user has published and study how these profiles change over time. Moreover, we evaluate the quality of the user modeling strategies in the context of personalized recommender systems and show that those strategies which consider the temporal dynamics of the individual profiles allow for the best performance.

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The diffusion of nanomedicines used to treat tumors is severely hindered by the microenvironment, which is a challenge that has emerged as a bottleneck for the effective outcome of nanotherapies. Classical strategies for enhancing tumor penetration rely on passive movement in the extracellular matrix (ECM). Here, we demonstrate that nanomedicine also penetrates tumor lesions via an active trans-cell transportation process. This process was discovered by directly observing the movement of nanoparticles between cells, evaluating the intracellular trafficking pathway of nanoparticles via Rab protein labeling, comparing endocytosis-exocytosis between nanoparticles administered with inhibitors, and correlating the transcytosis process with the micro-CT distribution of nanomedicines. We also demonstrated that enhanced tumor penetration promotes the therapeutic efficacy of a photodynamic therapeutic nanomedicine. Our research thus suggests that transcytosis could be an important positive factor for designing cancer nanomedicines.