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DNA-Biofunctionalization of CTAC-Capped Gold Nanocubes
2020, Slesiona, Nicole, Thamm, Sophie, Stolle, H. Lisa K.S., Weißenborn, Viktor, Müller, Philipp, Csáki, Andrea, Fritzsche, Wolfgang
Clinical diagnostics and disease control are fields that strongly depend on technologies for rapid, sensitive, and selective detection of biological or chemical analytes. Nanoparticles have become an integral part in various biomedical detection devices and nanotherapeutics. An increasing focus is laid on gold nanoparticles as they express less cytotoxicity, high stability, and hold unique optical properties with the ability of signal transduction of biological recognition events with enhanced analytical performance. Strong electromagnetic field enhancements can be found in close proximity to the nanoparticle that can be exploited to enhance signals for e.g., metal-enhanced fluorescence or Raman spectroscopy. Even stronger field enhancements can be achieved with sharp-edged nanoparticles, which are synthesized with the help of facet blocking agents, such as cetyltrimethylammonium bromide/chloride (CTAB/CTAC). However, chemical modification of the nanoparticle surface is necessary to reduce the particle’s cytotoxicity, stabilize it against aggregation, and to bioconjugate it with biomolecules to increase its biocompatibility and/or specificity for analytical applications. Here, a reliable two-step protocol following a ligand exchange with bis (p-sulfonatophenyl) phenyl phosphine (BSPP) as the intermediate capping-agent is demonstrated, which results in the reliable biofunctionalization of CTAC-capped gold nanocubes with thiol-modified DNA. The functionalized nanocubes have been characterized regarding their electric potential, plasmonic properties, and stability against high concentrations of NaCl and MgCl2.
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection
2022, Alam, Mehwish, Iana, Andreea, Grote, Alexander, Ludwig, Katharina, Müller, Philipp, Paulheim, Heiko, Laforest, Frédérique, Troncy, Raphael, Médini, Lionel, Herman, Ivan
News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users' selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items' stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users' opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy.